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We assess the ability of variants of anomalous change detection (ACD) to identify human activity associated with large outdoor music festivals as they are seen from synthetic aperture radar (SAR) imagery collected by the Sentinel-1 satellite constellation. We found that, with appropriate feature vectors, ACD using random-forest machine learning was most effective at identifying changes associated with the human activity.
The pursuit of fusion energy has required extensive experimental and theoretical science activities to develop the knowledge needed that will enable design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned to enable optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. Jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR), a workshop was organized to identify Priority Research Opportunities (PRO’s) for application of ML/AI methods to enable accelerated solution of fusion problems. The resulting “Advancing Fusion with Machine Learning Research Needs Workshop,” held in Gaithersburg, MD, April 30 – May 2, 2019, brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, to identify key PRO’s.
Ethanol is the smallest molecule containing C–O, C–C, C–H, and O–H bonds present in biomass-derived oxygenates. The development of inexpensive and selective catalysts for ethanol reforming is important towards the renewable generation of hydrogen from biomass. Transition metal nitrides (TMN) are interesting catalyst support materials that can effectively reduce precious metal loading for the catalysis of ethanol and other oxygenates. Herein theoretical and experimental methods were used to probe platinum-modified molybdenum nitride (Pt/Mo 2 N) surfaces for ethanol reforming. Computations using density-functional theory and machine learning predicted monolayer Pt/Mo 2 N to be highly active and selective for ethanol reforming. Temperature-programmed desorption (TPD) experiments verified that ethanol primarily underwent decomposition on Mo 2 N, and the reaction pathway shifted to reforming on Pt/Mo 2 N surfaces. Additionally, high-resolution electron energy loss spectroscopy (HREELS) results further indicated that while Mo2N decomposed the ethoxy intermediate by cleaving C–C, C–O, and C–H bonds, Pt-modification preserved the C–O bond, resulting in ethanol reforming.
The Mars Program has recently been informed of the Planetary Protection Subcommittee (PPS) recommendation, which was endorsed by the NAC, concerning Mars Science Lab (MSL) lessons learned and knowledge capture. The Mars Program has not had an opportunity to consider any decisions specific to the PPS recommendation. Some of the activities recommended by the PPS would involve members of the MSL flight team who are focused on cruise, entry descent & landing, and early surface operations; those activities would have to wait. Members of the MSL planetary protection team at JPL are still available to support MSL lessons learned and knowledge capture; some of the specifically recommended activities have already begun. The Mars Program shares the PPS/NAC concerns about loss of potential information & expertise in planetary protection practice.
Based on several magnetic nonpotentiality parameters obtained from the vector photospheric active region magnetograms obtained with the Solar Magnetic Field Telescope at the Huairou Solar Observing Station over two solar cycles, a machine learning model has been constructed to predict the occurrence of flares in the corresponding active region within a certain time window. The Support Vector Classifier, a widely used general classifier, is applied to build and test the prediction models. Several classical verification measures are adopted to assess the quality of the predictions. We investigate different flare levels within various time windows, and thus it is possible to estimate the rough classes and erupting times of flares for particular active regions. Several combinations of predictors have been tested in the experiments. The True Skill Statistics are higher than 0.36 in 97% of cases and the Heidke Skill Scores range from 0.23 to 0.48. The predictors derived from longitudinal magnetic fields do perform well, however, they are less sensitive in predicting large flares. Employing the nonpotentiality predictors from vector fields improves the performance of predicting large flares of magnitude {>=}M5.0 and {>=}X1.0.
Westinghouse has accumulated significant know-how in the reactor dismantling activities. The approach to internals segmentation and packaging has been continuously enhanced by incorporating lessons learned and best practices into each successive project. Building on the most recent successful experience at Jose Cabrera in Spain, Westinghouse has won several major awards with 13 reactors currently under contract at Chooz A (France), Barsebaeck 1-2 (Sweden), Bohunice V1 (two units in Slovakia), Neckarwestheim I, Philippsburg 1, and more recently at Unterweser, Grafenrheinfeld, Grohnde, Isar 1, Isar 2, Brokdorf (Germany). The primary challenges of a segmentation and packaging project are to separate the highly activated materials from the less-activated materials and package them into appropriate containers for disposal. The choice of the optimum cutting technology is important for a successful project implementation and depends on some specific constraints like disposal costs, project schedule, available work areas or safety. The selected technology for all recent Westinghouse projects is mechanical cutting with different types of designed cutting equipment (hydraulic shears, band saw, disc saw). All segmentation tools are remotely controlled due to the high radiation levels and ALARA and personal safety is the number one priority during the challenging site work. Training on mock-ups in the Westinghouse facility before going to the site is a very important step in order to optimize all procedures and thereby maximize the efficiency on site in order to finalize the project on time or before. The work covers the segmentation of reactor internals but also, in some cases, some replaced components during plant operation that were stored in the spent fuel pool. The cutting equipment proved to be reliable and flexible which is very valuable for the execution of such a challenging project. The Barsebaeck Unit 2 site work has been finished end of October 2017 and its lessons learned are taken into consideration for future activities. Site work is currently on-going simultaneously at Chooz A, Barsebaeck 1, Neckarwestheim I and Philippsburg 1. This paper will provide an overview of all these on-going segmentation projects and will focus on feedback from the first projects executed on site. (authors)
Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.
Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.
The successful utilization of small satellites for scientific missions relies on continual infusion of technology innovations, creative approaches, and new capabilities, all of which advance at a very rapid pace. Achieving these advancements requires open and efficient exchange of results, experiences, and ideas. Community learning is particularly challenging in this fast-growing community involving an increasingly diverse set of players from all sectors: industry, academia, government, and the public at large. Building and cultivating a community of practices around small satellite technology development and mission implementation are key objectives for NASA’s Small Spacecraft Systems Virtual Institute (S3VI). The institute has developed and provides access to a large collection of products, tools, and activities to advance clear communications and coordination regarding small spacecraft undertakings across NASA, to provide mission enabling information to the smallsat research community, to engage with stakeholders in industry, government, academia and the general public, and to support the overall small spacecraft community. New and updated offerings by the S3VI include: The2021 NASA State of the Art Report of Small Spacecraft Technology, the Small Spacecraft Reliability Initiative Knowledge Base, and the “MISSION ACCOMPLISHED”webinar series. These and other institute products will help scientists and engineers planning future missions answer pertinent questions, such as: What is the state of the art of small spacecraft technology that can be used?What are some best practices that other experts and teams can recommend? What did previous missions accomplish? What lessons could be learned from previous missions? What flight-proven parts are available? What emerging technologies could be taken advantage of? A status will be presented on S3VI activities facilitating community learning within the small satellite science community through the collection, sharing, and exchange of experiences with small satellite mission development and execution across all NASA mission areas.
The successful utilization of small satellites for scientific missions relies on continual infusion of technology innovations, creative approaches, and new capabilities, all of which advance at a very rapid pace. Achieving these advancements requires open and efficient exchange of results, experiences, and ideas. Community learning is particularly challenging in this fast-growing community involving an increasingly diverse set of players from all sectors: industry, academia, government, and the public at large. Building and cultivating a community of practices around small satellite technology development and mission implementation are key objectives for NASA’s Small Spacecraft Systems Virtual Institute (S3VI). The institute has developed and provides access to a large collection of products, tools, and activities to advance clear communications and coordination regarding small spacecraft undertakings across NASA, to provide mission enabling information to the smallsat research community, to engage with stakeholders in industry, government, academia and the general public, and to support the overall small spacecraft community. New and updated offerings by the S3VI include: The2021 NASA State of the Art Report of Small Spacecraft Technology, the Small Spacecraft Reliability Initiative Knowledge Base, and the “MISSION ACCOMPLISHED” webinar series. These and other institute products will help scientists and engineers planning future missions answer pertinent questions, such as: What is the state of the art of small spacecraft technology that can be used? What are some best practices that other experts and teams can recommend? What did previous missions accomplish? What lessons could be learned from previous missions? What flight-proven parts are available? What emerging technologies could be taken advantage of? A status will be presented on S3VI activities facilitating community learning within the small satellite science community through the collection, sharing, and exchange of experiences with small satellite mission development and execution across all NASA mission areas.
The Defense-Related Uranium Mines (DRUM) Program provides the structure and basis for the US Department of Energy Office of Legacy Management (LM) to manage the verification and validation (V and V) of defense-related uranium mines (mines) that provided ore to the Atomic Energy Commission from 1947 to 1970. V and V activities are conducted to fully understand the scope of potential problems posed by these mines by determining their location, reclamation or remediation status, and potential impacts to public safety, human health, and the environment. Land management agencies have limited resources and have generally not conducted comprehensive inventories of abandoned uranium mines, thus adding to the need to undertake this program. LM has entered into memoranda of understanding (MOUs) with the US Bureau of Land Management (BLM) and US Forest Service (USFS) to facilitate a better understanding of the scope of the problem posed by these mines. Currently, LM has executed MOUs with the BLM state offices in Colorado, New Mexico, Utah, and Wyoming and the USFS Rocky Mountain Region, which includes the national forests in Colorado, Wyoming, and South Dakota. This collaborative effort with partner agencies has led LM to develop a V and V Work Plan that describes the numerous activities and types of data to be collected at the mines. V and V activities verify actual mine location, collect coordinates of mining-related features, collect radiological data and soil samples, identify potential physical safety hazards, prioritize mines using a risk-based screening approach, and report the information to partner agencies. The partner agencies may use the reports to assess priorities and determine if any further action is warranted. The DRUM Program goals are to: - Improve data quality and content in the LM national inventory of abandoned uranium mines; - Conduct site-specific reconnaissance at mines for data verification and validation; - Exchange mine information with other federal agencies and state governments to help address mines presenting the greatest risks; - Develop reports for each mine that will contain a risk scoring assessment to help each agency develop priorities. The preliminary risk screening methodology was based on a joint effort between LM and BLM to develop factors that would be based on field observations, field data, and laboratory data using a multiple-lines-of-evidence approach. The multiple-lines-of-evidence assessment allows land managers the opportunity and flexibility to query specific criteria and make rankings based on specific priorities. The risk screening for each mine is based on evaluating the primary hazards of physical safety and risks to human health and several modifying factors that include accessibility and ecological/environmental risks. Gamma radiation scans are performed across the disturbed area of the mine, and soil samples are taken from waste rock piles and drainages, when applicable. This paper presents the results of performing inventory, environmental sampling, and risk ranking along with other accomplishments and lessons learned during the initial year of V and V activities and reporting. (authors)
Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.
In the last few years, we have seen the rise of deep learning applications in a broad range of computational chemistry research problems. Using human-engineered chemical features, such as molecular descriptors and fingerprints, deep learning models have shown similar, if not better performance that most traditional machine learning algorithms. Recently, we reported on the development of Chemception, a deep convolutional neural network (CNN) architecture for general-purpose small molecule property prediction. On average, Chemception matched the performance of expert-developed QSAR/QSPR models trained on chemical features (molecular fingerprints), despite that it was trained on just 2D images of molecular drawings with minimal chemical information. Here, we investigate the effects of systematically removing and adding basic chemical information to the image channels of the 2D images used to train Chemception. By augmenting our images with only 3 additional basic chemical information, we demonstrate the improvement of Chemception performance – that it is now more accurate than contemporary deep learning models trained on ECFP fingerprints for the prediction of toxicity, activity and solvation free energy, as well as physics-based free energy simulation methods for computing solvation properties. By altering the chemical information content in the image channels, and examining the resulting performance of Chemception, we also identify to two different “learning patterns” in toxicity/activity as compared to solvation free energy, and it parallels the fundamental differences in contemporary chemistry research for predicting toxicity/activity and solvation free energy.
Nuwu (Southern Paiute), Newe (Western Shoshone), and Nuumu (Owens Valley Paiute) are linguistically related, Numic-speaking peoples who are part of the broader Uto-Aztecan language group. Numic peoples view the land as a holistic, living, sentient being with feelings and purpose. The land is personified with human characteristics and it needs to be experienced to be understood through “learning by doing.” Numic peoples do not support ground disturbing activities within their ancestral lands, including activities tied to the storage of low-level radioactive waste or classified materials on the NNSS, which they view as culturally inappropriate. These deep-rooted ancestral connections are the impetus for reinforcing Numic responsibility for healing disturbed areas by integrating respect and patience with consistent Tribal interaction. Tribal Ecological Knowledge (TEK) is the science of describing Tribal approaches for understanding natural resources. Numic TEK is embedded in traditional teachings learned incrementally over time though experience and it evolves through lessons learned and responses to environmental changes over millennia. Therefore, TEK can broaden and enhance Western scientific knowledge associated with revegetation, especially in highly disturbed areas. The project blended TEK with Western scientific ecological methods to create a vegetative cover within test plots on the 92-Acre Area located at the Radioactive Waste Management Complex (RWMC) located in Area 5 on the Nevada National Security Site (NNSS). The vegetated test plots were systematically created for the Department of Energy (DOE) in tandem with the existing Federal Facilities Agreement and Consent Order (FFACO) with the Nevada Department of Environmental Protection (NDEP). Three previous contractor-lead attempts at revegetation, one targeting full cover revegetation and two targeting test plot revegetation, did not achieve the anticipated results at this location. When presented to the 16 American Indian Tribal nations and affiliated groups with cultural and historical ties to the NNSS, the group appointed a Tribal Revegetation Committee (TRC) that included six expert Tribal knowledge holders to collaborate with an ethnoecologist/cultural anthropologist and two biologists. Project design, planning, seed and outplant selection, spiritual land preparation, and methodology were guided by the TRC and an ethnoecologist/cultural anthropologist from Portland State University (PSU) and biologists from Desert Research Institute (DRI). Using TEK, the TRC recommended a specific seed mixture that contained nine native plant species and three species of outplants. The revegetation effort included preparing and planting thirty 10 m × 10 m (32.8 ft × 32.8 ft) seeded plots, twelve of which also included outplants; and eight 10 m × 100 m (32.8 ft × 328 ft) plots that only received outplants, all atop a waste cell cover. The TRC and the project team creatively adapted TEK with Western scientific methods so that the planned revegetation efforts could occur within the safety and security parameters of the RWMC. Test plots were subjected to one of five soil treatments with varying combinations of straw or mulch applications, soil amendment, and/or outplant planting and one of two watering treatments (watered or unwatered). Planting was divided into two events: one in the fall season and another during the subsequent spring season based on TEK and a corresponding recommendation from the TRC. Monitoring and spiritual management was conducted by the TRC to evaluate plant progress on a monthly basis (in conjunction with the biologist and anthropologist) during each respective growing season for a period of three years after planting. This approach allowed Tribal members the opportunity to conduct traditional blessings and other culturally appropriate activities to restore balance to the land in accordance with Tribal protocols. Following TEK-guided methods, successful plant establishment from seed stock and outplants was observed in many plots. Overall, plots planted in the spring, as recommended by the TRC, showed higher rates of outplant survival and native seedling emergence than those planted in the fall. This finding is significant because it is contrary to the original guidance and previous approaches provided for planting in this region. The TRC believes the frequent co-occurrence of native seedlings near surviving outplants indicates an important symbiotic relationship understood by Tribal communities and overlooked by others. Watered outplants displayed much higher survivability than unwatered plants, even after watering was reduced after the plants were established. Soil amendments and mulch created higher densities of native plants from seed. Many native seedlings showed significant delays in germination, which is considered a normal adaptation to desert climates. Some native plants did not germinate until the third growing year, whereas others germinated during the first growing year, which demonstrates the complexity of the desert environment. Evidence of native insects, reptiles, mammals, and birds, as well as native plants that were not part of the planted species, were noted and considered culturally significant. Despite the presence of non-native plants, native outplants continued to thrive and the incidence of native plant germination from seed increased over time. These successful revegetation results where previous efforts were unsuccessful reinforce the importance of integrating regionally appropriate, TEK-guided methodology with Western science to achieve positive results and the necessity of integrating Tribal involvement in all stages of the revegetation effort. Expanded approaches coupled with Tribal knowledge and tools from Western science addressed a complex problem tied to revegetating atop a low-level radioactive waste cell. The level of Tribal participation serves as a progressive model for building collaborative relationships and addressing ecological challenges on the NNSS.
Rational catalyst design remains a significant challenge, with electronic structure, steric, and electrostatic effects known to contribute to activity. Recently, dynamics has been recognized as another factor that impacts catalysis, though identifying and predicting these effects has remained out of reach. Nickel-substituted rubredoxin (NiRd), a protein-based mimic of a hydrogenase enzyme, serves as a model catalytic system in which dynamics can be systematically investigated with respect to activity. While over 30 secondary-sphere mutants of NiRd have been shown to be catalytically active, no significant correlation was observed between the rates and catalytic overpotential or electronic structure, prompting questions about the protein-derived factors that modulate activity. Here, in this work, NMR spectroscopy was used to investigate the roles of substrate accessibility, protein dynamics, and protein stability in controlling catalysis. Significant paramagnetic effects from the nickel center (S = 1) isolate the methylene proton resonances of the metal-coordinating cysteine residues. The sensitivity of resonance positions and linewidths to local environment offers an opportunity to study dynamical molecular changes around the metal center with high resolution. Machine learning algorithms were employed to identify correlations between the catalytic activity and the paramagnetic NMR spectra. These analyses revealed spectroscopic features of specific cysteine protons that report on catalytic overpotential and increased turnover rates, which are further supported by the results obtained using high-field NMR techniques. Collectively, these studies indicate the potential for multifrequency NMR techniques to resolve key contributors to catalytic activity and highlight the importance of local and outer-sphere dynamics.
Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.
The International Space Station has many fluid systems on the United States On-orbit Segment supporting thermal control, life support, extravehicular activities and payloads. To facilitate assembly and maintenance, fluid quick disconnect (QD) fittings are used throughout these systems for both internal pressurized modules and for external systems exposed to space. In the years since full scale development began on these QDs, a number of design and performance issues have surfaced and required substantial program funds and activity to rectify. This paper is intended to describe the most significant of these issues, their resolutions, and the lessons learned from the ISS experience.
Microbial metabolism influences rates of net community production (NCP), exerting a direct biological control on marine oxygen and carbon fluxes. In the Arctic, it is increasingly important to understand and quantify this process, as ecological and oceanographic conditions shift due to changing climate. Here, we describe potential ecological links between pelagic microbial diversity and an NCP precursor, biological oxygen utilization, using machine learning and paired observations of community structure and metabolic activity from a seasonally and spatially variable transect of the Arctic Ocean (2019–2020 MOSAiC Expedition). Community structure was determined using 16S (prokaryotic) and 18S (eukaryotic) rRNA gene amplicon sequencing, and metabolic activity was derived from ΔO 2 /Ar. Using self-organizing maps, we identified clear successional patterns in observed microbial community structure that were seasonally driven in the upper ocean and vertically stratified with depth. Metabolic activity was also stratified, with a primarily net heterotrophic water column (median −1.5% biological oxygen saturation), excepting periodic oxygen supersaturation (maximum: 13.6%) within the mixed layer. Using DNA sequences as predictor variables, we then constructed a random forest regression model that reliably reconstructed biological oxygen concentrations (root mean squared error = 4.14 μmol kg −1 ). Top predictors from this model were from heterotrophic (bacteria) or potentially mixotrophic (dinoflagellate) taxa. These analyses highlight biologically driven diagnostic tools that can be used to expand biogeochemical datasets and improve the microbial perspectives and metabolisms represented in ecological models of net productivity and carbon flux in a changing Arctic Ocean.