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At least 991 records · Page 55

Eight new luminous z ≥ 6 quasars discovered via SED model fitting of VISTA, WISE and Dark Energy Survey Year 1 observations

We present the discovery and spectroscopic confirmation with the ESO NTT and Gemini South telescopes of eight new 6.0 < z < 6.5 quasars with z$_{AB}$ < 21.0. These quasars were photometrically selected without any star-galaxy morphological criteria from 1533 deg$^{2}$ using SED model fitting to photometric data from the Dark Energy Survey (g, r, i, z, Y), the VISTA Hemisphere Survey (J, H, K) and the Wide-Field Infrared Survey Explorer (W1, W2). The photometric data was fitted with a grid of quasar model SEDs with redshift dependent Lyman-{\alpha} forest absorption and a range of intrinsic reddening as well as a series of low mass cool star models. Candidates were ranked using on a SED-model based $\chi^{2}$-statistic, which is extendable to other future imaging surveys (e.g. LSST, Euclid). Our spectral confirmation success rate is 100% without the need for follow-up photometric observations as used in other studies of this type. Combined with automatic removal of the main types of non-astrophysical contaminants the method allows large data sets to be processed without human intervention and without being over run by spurious false candidates. We also present a robust parametric redshift estimating technique that gives comparable accuracy to MgII and CO based redshift estimators. We find two z $\sim$ 6.2 quasars with HII near zone sizes < 3 proper Mpc which could indicate that these quasars may be young with ages < 10$^6$ - 10$^7$ years or lie in over dense regions of the IGM. The z = 6.5 quasar VDESJ0224-4711 has J$_{AB}$ = 19.75 is the second most luminous quasar known with z > 6.5.

79 ASTRONOMY AND ASTROPHYSICS↗

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

Geographic information system for fusion and analysis of high-resolution remote sensing and ground truth data

We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System, integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a bore Al forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case I) calibrated DC-8 SAR data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case II) will produce calibrated DC-8 SAR and AVIRIS data, together with extensive measurements on the tropical rain forest itself. The extreme range of these sites, one an Arctic forest, the other a tropical rain forest, has been deliberately chosen to find common problems which can lead to generalized observations and unique problems with data which raise issues for the EOS System.

Freeman, Anthony↗

Geographic information system for fusion and analysis of high-resolution remote sensing and ground data

We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System (GIS), integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a boreal forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case 1) calibrated DC-8 SAR (Synthetic Aperture Radar) data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case 2) will produce calibrated DC-8 SAR and AVIRIS data, together with extensive measurements on the tropical rain forest itself. The extreme range of these sites, one an Arctic forest, the other a tropical rain forest, has been deliberately chosen to find common problems which can lead to generalized observations and unique problems with data which raise issues for the EOS System.

Freeman, Anthony↗

Quasar Absorption in the UV: Probing the Intergalactic Medium

The purpose of this project is to model the low-redshift Lyman-alpha forest and exploration of the relation between Lyman-alpha absorbers and galaxies. This paper shows that the simulation models that are so successful at explaining properties of the high-redshift forest also account for the most important results of observational studies of the low-redshift forest, from HST (especially the Quasar Absorption Line Key Project) and ground-based follow-up.

Weinberg, David↗

Consequences of Altered Root Nutrient Uptake for Soil Carbon Stabilization (Final Report)

Objectives: The primary objectives of this project are to improve our understanding of tropical forest belowground processes, and to increase representation of this biome in ecosystem-scale and global C cycle models. Belowground dynamics present a major source of uncertainty inhibiting our ability to predict C cycle responses to climate change. Representation of tropical forests in these models is particularly lacking, even though these ecosystems hold >25% of terrestrial C stocks. While the majority of humid tropical forests exist on relatively infertile soils (i.e. poor in rock-derived nutrients), enormous gradients in soil nutrient availability exist at landscape-scales due to shifts in geology and soil order. Soil fertility is likely to greatly influence how tropical soil C storage will respond to the declines in precipitation predicted for the tropics. In particular, root dynamics vary across soil fertility gradients, with roots representing the major input of C to subsurface soils. Root characteristics related to soil fertility include biomass, turnover, C exudates, tissue chemistry, and nutrient uptake rates, with each of these likely sensitive to changes in moisture. The proposed project will fully integrate field research with an existing ecosystem model to assess potential effects of drying on belowground tropical C dynamics across a range of soil fertilities. Project Description:. This project will identify key linkages among soil fertility, root nutrient uptake, root dynamics, and soil C storage. The hypothesized framework for understanding these linkages is: Tropical rainforest roots in relatively fertile soils have lesser root biomass, faster turnover, fewer exudates, and improved root tissue quality relative to infertile sites, all resulting from increased root nutrient uptake. Ultimately, these root characteristics in fertile soils promote proportionally greater long-term soil C storage in organo-mineral associations. Specifically, lower root exudates and improved root tissue quality in fertile soils reduce microbial respiration of extant soil C (i.e. “priming) and increase microbial C use efficiency, leading to sorption of protein-rich microbial and plant organic matter on soil mineral surfaces. However, the larger pools of mineral-associated C in fertile tropical soils are more vulnerable to decreased rainfall than smaller C pools in infertile soils, in part because of the lesser root biomass, faster turnover, and softer, more labile root tissues. Also, the shallower lateral rooting structures typical in fertile soils experience greater desiccation and death than deeper roots in infertile sites, with little short-term adaptation to drying.

54 ENVIRONMENTAL SCIENCES↗

Integrating Biomass Conversion Technologies with Recovery Operations In-Woods: Modeling Supply Chain

Economic potential of feedstock generated low-valued forest residue can be enhanced by emerging biomass conversion technologies (BCT), such as torrefaction, briquetting, and gasification. However, for implementing these emerging processes within the woods, several hurdles are to be overcome, among which a balanced supply chain is pivotal. Centralized biomass recovery operation (CBRO) could be an economically viable solution in accessing harvesting sites and allows integration of BCT into forest management. The goal of this study was to examine the logistic effects of integrating a BCT into a CBRO, under different in-wood scenarios based on variations in travel time between the facility locations, amount of raw materials handled, intermediate storage capacity, and duration (number of days) of annual operations. Specific objectives included analyzing the effects of forest residue recoverability (BDMT, bone dry metric ton/ha), total transportation time from the harvest unit to the market, and the annual number of in-woods production sites on the overall efficiency of the BCT operations. Concurrently, this study examined the forest managerial impacts due to such an integration. Location-allocation tool (maximize market share problem type) within the ArcGIS Network Analyst platform was utilized to model the scenarios and generate one-way travel times from the harvest site to final markets. Results from geospatial analysis showed that there were 89–159 and 64–136 suitable locations for the BCT for logistics model (LM) I and II, respectively. Total one-way travel time for all the models ranged between 1.0–1.7 h. Additionally, the annual numbers of BCT sites was inversely proportional to the total one-way travel time (i.e., harvest unit to market). Arranging CBRO and BCT operations to occur at the same in-woods site returned shorter total and average travel times than arranging the two activities at separate in-woods sites. The model developed for this study can be used by forest managers and entrepreneurs to identify sites for placing BCTs in the forest that minimizes transportation times.

Paulson, Jeffrey↗

Forest and wildlife habitat analysis using remote sensing and geographic information systems

Forest and wildlife habitat analyses were conducted at the H.J. Andrews Experimental Forest in the Central Cascade Mountains of Oregon using remotely sensed data and a geographic information system (GIS). Landsat Thematic Mapper (TM) data were used to determine forest successional stages, and to analyze the structure of both old and young conifer forests. Two successional stage maps were developed. One was developed from six TM spectral bands alone, and the second was developed from six TM spectral bands and a relative sun incidence band. Including the sun incidence band in the classification improved the mapping accuracy in the two youngest successional stages, but did not improve overall accuracy or accuracy of the two oldest successional stages. Mean spectral values for old-growth and mature stands were compared in seven TM bands and seven band transformations. Differences between mature and old-growth successional stages were greatest for the band ratio of TM 4/5 (P = 0.00005) and the multiband transformation of wetness (P = 0.00003). The age of young conifer stands had the highest correlation to TM 4/5 values (r = 0.9559) of any of the TM band or band transformations used. TM 4/5 ratio values of poorly regenerated conifer stands were significantly different from well regenerated conifer stands after age 15 (P = 0.0000). TM 4/5 was named a 'Successional Stage Index' (SSI) because of its ability to distinguish forest successional stages. The forest successional stage map was used as input into a vertebrate richness model using GIS. The three variables of (1) successional stage, (2) elevation, and (3) site moisture were used in the GIS to predict the spatial occurrence of small mammal, amphibian, and reptile species based on primary and secondary habitat requirements. These occurrence or habitat maps were overlayed to tally the predicted number of vertebrate at any given point in the study area. Overall, sixty-three and sixty-seven percent of the model predictions for vertebrate occurrence matched the vertebrates that were trapped in the field in eight forested stands. Of the three model variables, site moisture appeared to have the greatest influence on the pattern of high vertebrate richness in all vertebrate classes.

Fiorella, Maria↗

Cryptic Methane Emissions from Upland Forest Ecosystems

This exploratory research on Cryptic Methane Emissions from Upland Forest Ecosystems was motivated by evidence that upland ecosystems emit 36% as much methane to the atmosphere as global wetlands, yet we knew almost nothing about this source. The long-term objective was to refine Earth system models by quantifying methane emissions from upland forests, and elucidate the biogeochemical processes that govern upland methane emissions. The immediate objectives of the grant were to: (i) test the emerging paradigm that upland trees unexpectedly transpire methane, (ii) test the basic biogeochemical assumptions of an existing global model of upland methane emissions, and (iii) develop the suite of biogeochemical approaches that will be needed to advance research on upland methane emissions. We instrumented a temperate forest system in order to explore the processes that govern upland methane emissions. We demonstrated that methane is emitted from the stems of dominant tree species in temperate upland forests. Tree emissions occurred throughout the growing season, while soils adjacent to the trees consumed methane simultaneously, challenging the concept that forests are uniform sinks of methane. High frequency measurements revealed diurnal cycling in the rate of methane emissions, pointing to soils as the methane source and transpiration as the most likely pathway for methane transport. We propose the forests are smaller methane sinks than previously estimated due to stem emissions. Stem emissions may be particularly important in upland tropical forests characterized by high rainfall and transpiration, resolving differences between models and measurements. The methods we used can be effectively implemented in order to determine if the phenomenon is widespread.

54 ENVIRONMENTAL SCIENCES↗

Using Ecosystem Experiments to Improve Vegetation Models

Ecosystem responses to rising CO2 concentrations are a major source of uncertainty in climate change projections. Data from ecosystem-scale Free-Air CO2 Enrichment (FACE) experiments provide a unique opportunity to reduce this uncertainty. The recent FACE Model–Data Synthesis project aimed to use the information gathered in two forest FACE experiments to assess and improve land ecosystem models. A new 'assumption-centred' model intercomparison approach was used, in which participating models were evaluated against experimental data based on the ways in which they represent key ecological processes. Identifying and evaluating the main assumptions caused differences among models, and the assumption-centered approach produced a clear roadmap for reducing model uncertainty. We explain this approach and summarize the resulting research agenda. We encourage the application of this approach in other model intercomparison projects to fundamentally improve predictive understanding of the Earth system.

54 ENVIRONMENTAL SCIENCES↗

AmeriFlux US-NR1 Niwot Ridge Forest (LTER NWT1)

This is the AmeriFlux version of the carbon flux data for the site US-NR1 Niwot Ridge Forest (LTER NWT1). Site Description - The Niwot Ridge US-NR1 AmeriFlux site is located in a subalpine forest ecosystem 8 km east of the Continental Divide near Nederland, CO. The site is located at 3050 m elevation, within 600m of the LTER C-1 long-term monitoring station. The subalpine forest at the site was selectively logged in the early 1900s and the forest has not had any major disturbance since then (Burns, S.P., (2018), The Influence of Warm-Season Precipitation on Water Cycling and the Surface Energy Budget within and just-above a Colorado Subalpine Forest in Mountainous Terrain: Measurements and Modeling, PhD Thesis, https://scholar.colorado.edu/geog_gradetds/125/). Additional data, photos, and information can be found at: Burns S. P., P. D. Blanken, and R. K. Monson (2020): Data, Photographs, Videos, and Information for the Niwot Ridge Subalpine Forest (US-NR1) AmeriFlux site. AmeriFlux Management Project. https://doi.org/10.15485/1671825

Blanken, Peter D.↗

AmeriFlux FLUXNET-1F US-NR1 Niwot Ridge Forest (LTER NWT1)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-NR1 Niwot Ridge Forest (LTER NWT1). This is the FLUXNET version of the carbon flux data for the site US-NR1 Niwot Ridge Forest (LTER NWT1) produced by applying the standard ONEFlux (1F) software. Site Description - The Niwot Ridge US-NR1 AmeriFlux site is located in a subalpine forest ecosystem 8 km east of the Continental Divide near Nederland, CO. The site is located at 3050 m elevation, within 600m of the LTER C-1 long-term monitoring station. The subalpine forest at the site was selectively logged in the early 1900s and the forest has not had any major disturbance since then (Burns, S.P., (2018), The Influence of Warm-Season Precipitation on Water Cycling and the Surface Energy Budget within and just-above a Colorado Subalpine Forest in Mountainous Terrain: Measurements and Modeling, PhD Thesis, https://scholar.colorado.edu/geog_gradetds/125/). Additional data, photos, and information can be found at: Burns S. P., P. D. Blanken, and R. K. Monson (2020): Data, Photographs, Videos, and Information for the Niwot Ridge Subalpine Forest (US-NR1) AmeriFlux site. AmeriFlux Management Project. https://doi.org/10.15485/1671825

Blanken, Peter D.↗

Model Inputs, Outputs, and Scripts associated with: “Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin”

This data package is associated with the publication “Combined effects of stream hydrology and land use on basin‐scale hyporheic zone denitrification in the Columbia River Basin”, published in Water Resource Research (Son et al.2022) available at https://doi.org/10.1029/2021WR031131. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) denitrification at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions, including two-step denitrification and aerobic respiration, are considered within the HZ. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values. This study uses the RCM to explore the spatial patterns of HZ denitrification across reaches with different sizes and land use in the CRB. Our main objective is to use the RCM as a virtual reality model, and the machine-learning models as surrogates that encapsulate the complexities of the physics-based model while identifying the importance of different variables that are not evident in the model conceptualization. We do not include a direct comparison of the modeled HZ denitrification and measurements; however, the RCM can capture the overall spatial patterns of the HZ denitrification because the model inputs and its reaction networks are based on well-established theory and a physical-based model. The combination of the model-based predictions and a machine-learning approach (e.g., random forest) is used to improve our understanding of what variables of the model are associated with spatial patterns of the modeled denitrification across reaches with different sizes and land uses, and to develop a proxy model using measurable variables to reproduce the simulated patterns.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.

54 ENVIRONMENTAL SCIENCES↗

Sensitivity of Regional Climate to Deforestation in the Amazon Basin

The deforestation results in several adverse effect on the natural environment. The focus of this paper is on the effects of deforestation on land-surface processes and regional climate of the Amazon basin. In general, the effect of deforestation on climate are likely to depend on the scale of the defrosted area. In this study, we are interested in the effects due to deforestation of areas with a scale of about 250 km. Hence, a meso-scale climate model is used in performing numerical experiments on the sensitivity of regional climate to deforestation of areas with that size. It is found that deforestation results in less net surface radiation, less evaporation, less rainfall, and warmer surface temperature. The magnitude of the of the change in temperature is of the order 0.5 C, the magnitudes of the changes in the other variables are of the order of IO%. In order to verify some of he results of the numerical experiments, the model simulations of net surface radiation are compared to recent observations of net radiation over cleared and undisturbed forest in the Amazon. The results of the model and the observations agree in the following conclusion: the difference in net surface radiation between cleared and undisturbed forest is, almost, equally partioned between net solar radiation and net long-wave radiation. This finding contributes to our understanding of the basic physics in the deforestation problem.

Eltahir, Elfatih A. B.↗

Connections Between Stratospheric Pollution and the Asian Summer Monsoon

The Asian Monsoon leads to rapid vertical transport of gases and aerosols into the upper troposphere. Some of the pollution might be transported above cloud levels, which will allow it to spread globally and possibly at some occasions reach into the stratosphere. In this study we will use the GISS climate model to investigate the interactions between pollution and convective transport as well as secondary aerosol formation. Pollution resulting from anthropogenic activity as well as from natural sources such as small and large volcanic eruptions, dust storms and forest fires will be quantified. This modeling study will be accompanied by satellite observations from space that monitor aerosol optical thickness (AOT), and absorption AOT (AAOT) in two and three dimensions. Our goal is a better process level understanding of the evolution of natural and anthropogenic aerosol plumes in conjunction with the Asian Monsoon. Hence, we aim to explain their large-scale expansion, which eventually determines their impacts on climate.

Asia↗

Neutrino masses and cosmology with Lyman-alpha forest power spectrum

We present constraints on neutrino masses, the primordial fluctuation spectrum from inflation, and other parameters of the ΛCDM model, using the one-dimensional Lyα-forest power spectrum measured by [1] from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey (SDSS-III), complemented by Planck 2015 cosmic microwave background (CMB) data and other cosmological probes. This paper improves on the previous analysis by [2] by using a more powerful set of calibrating hydrodynamical simulations that reduces uncertainties associated with resolution and box size, by adopting a more flexible set of nuisance parameters for describing the evolution of the intergalactic medium, by including additional freedom to account for systematic uncertainties, and by using Planck 2015 constraints in place of Planck 2013. Fitting Lyα data alone leads to cosmological parameters in excellent agreement with the values derived independently from CMB data, except for a weak tension on the scalar index n{sub s}. Combining BOSS Lyα with Planck CMB constrains the sum of neutrino masses to ∑ m{sub ν} < 0.12 eV (95% C.L.) including all identified systematic uncertainties, tighter than our previous limit (0.15 eV) and more robust. Adding Lyα data to CMB data reduces the uncertainties on the optical depth to reionization τ, through the correlation of τ with σ{sub 8}. Similarly, correlations between cosmological parameters help in constraining the tensor-to-scalar ratio of primordial fluctuations r. The tension on n{sub s} can be accommodated by allowing for a running dn{sub s}/d ln k. Allowing running as a free parameter in the fits does not change the limit on ∑ m{sub ν}. We discuss possible interpretations of these results in the context of slow-roll inflation.

79 ASTRONOMY AND ASTROPHYSICS↗

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗

RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold

While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources.

59 BASIC BIOLOGICAL SCIENCES↗