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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Leveraging Application Data Constraints to Optimize Database-Backed Web Applications

Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present Coco, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, Coco leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, Coco employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that Coco can discover numerous data constraints from code analysis and improve real-world application performance significantly.

Computer Science↗
DOE OSTI2025

WELLS Interactive Application

The Wellbore Exploration and Location Logistic System (WELLS) Interactive Application is an interactive tool to enable easy exploration and visualization of the living national wellbore database (WELLS Database (https://edx.netl.doe.gov/dataset/wells_database)). The tool and underlying database were created and are maintained by the National Energy Technology Laboratory (NETL), providing visualization of the more than six million public wellbore records from more than 65 authoritative state, federal, and tribal resources. The WELLS Interactive Application serves up wellbore data from oil, gas, underground injection, research, geothermal, geotechnical, groundwater, and other types of wells in a single, standardized, unified system. In addition to the surface location of these wells, the underlying database combines select key attributes for features such as well age, depth, and operating status. The system also provides users with references back to the original sources used in this unified platform. The underlying data can be accessed through the WELLS Database: https://edx.netl.doe.gov/dataset/wells_database Additional Information: The WELLS Interactive Application (formerly titled CO2-Locate) enables visualization and access to the public wellbore records through an intuitive web-based mapping tool. The WELLS Interactive Application was designed to help users visualize, query, analyze, and download wellbore records. Public wellbore points are included as a layer in the Map page, called Public Wells. Additionally, a multivariate hexagon grid summarizing well density from proprietary well data, called Well Density, is included to identify data gaps between the public and proprietary well data. Filtering functionalities in the tool allow these two layers to be spatially filtered by state, county, or basin as well as by status, type, true vertical depth, and spud year. The WELLS Interactive Application also contains a Near Me tool can be used to search and explore wellbore data within a user-defined distance of a specified location on the map, which can also be downloaded. The Query tool allows users to query the selected or filtered wells in the Public Wells layer and export the data. For additional information on these tool functionalities, see the help documentation on the About page of the tool. Notes for Consideration: The Well Density layer provided in this application is derived from proprietary wellbore data, the records of which do not always contain values for key features (status, type, true vertical depth, or spud year). Therefore, data might not be available when layers are queried for all filter combinations. Additionally, visualizing layers and applying filters may take additional time to load (i.e., draw on the map) due to the large size of the data.

ccs↗
DOE OSTI2026

RCSB protein data Bank: Next‐generation advanced search for exploration of experimental structures and computed structure models

Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.

Rose, Yana [Research Collaboratory for Structural ↗
DOE OSTI2022

Using advanced data structures to enable responsive security monitoring

Write-optimized data structures (WODS), offer the potential to keep up with cyberstream event rates and give sub-second query response for key items like IP addresses. These data structures organize logs as the events are observed. To work in a real-world environment and not fill up the disk, WODS must efficiently expire older events. As the basis for our research into organizing security monitoring data, we implemented a tool, called Diventi, to index IP addresses in connection logs using RocksDB (a write-optimized LSM tree). In this work, we extended Diventi to automatically expire data as part of the data structures’ normal operations. We guarantee that Diventi always tracks the N most recent events and tracks no more than N + k events for a parameter k < N, while ensuring the index is opportunistically pruned. To test Diventi at scale in a controlled environment, we used anonymized traces of IP communications collected at SuperComputing 2019. We synthetically extended the 2.4 billion connection events to 100 billion events. We tested Diventi vs. Elasticsearch, a common log indexing tool. In our test environment, Elasticsearch saw an ingestion rate of at best 37,000 events/s while Diventi sustained ingestion rates greater than 171,000 events/s. Our query response times were as much as 100 times faster, typically answering queries in under 80 ms. Furthermore, we saw no noticeable degradation in Diventi from expiration. We have deployed Diventi for many months where it has performed well and supported new security analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Materials Provenance Store

Abstract We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper)

Molecular similarity search has been widely used in drug discovery to rapidly identify structurally similar compounds from large molecular databases. With the increasing size of chemical libraries, there is growing interest in the efficient ac- celeration of large-scale similarity search. Existing works mainly focus on CPU and GPU to accelerate the computation of Tatimoto coefficient in measuring the pairwise similarity between different molecular fingerprints. In this paper, we propose and optimize an FPGA-based accelerator design on exhaustive and approximate search algorithms. On exhaustive search using BitBound & fold- ing, we analyze the similarity cutoff and folding level relationship with search speedup and accuracy, and propose a scalable on- the-fly query engine on FPGAs to reduce the resource utilization and pipeline interval. We achieve a 450 million compounds-per- second processing throughput for a single query engine. On approximate search using hierarchical navigable small world (HNSW), a popular algorithm with high recall and query speed, we propose an FPGA-based graph traversal engine to utilize high throughput register array based priority queue and fine- grained distance calculation engine to increase the processing capability. Experimental results show that the proposed FPGA- based HNSW implementation achieves a 35× speedup than existing works on CPU. To the best of our knowledge, our FPGA- based implementation is the first attempt to accelerate molecular similarity search on FPGA and has the highest performance among existing approaches.

Peng, Hongwu↗
DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Unified many-worlds browsing of arbitrary physics-based animations

Manually tuning physics-based animation parameters to explore a simulation outcome space or achieve desired motion outcomes can be notoriously tedious. This problem has motivated many sophisticated and specialized optimization-based methods for fine-grained (keyframe) control, each of which are typically limited to specific animation phenomena, usually complicated, and, unfortunately, not widely used. In this paper, we propose Unified Many-Worlds Browsing (UMWB), a practical method for sample-level control and exploration of physics-based animations. Our approach supports browsing of large simulation ensembles of arbitrary animation phenomena by using a unified volumetric WORLDPACK representation based on spatiotemporally compressed voxel data associated with geometric occupancy and other low-fidelity animation state. Beyond memory reduction, the WORLDPACK representation also enables unified query support for interactive browsing: it provides fast evaluation of approximate spatiotemporal queries, such as occupancy tests that find ensemble samples ("worlds") where material is either IN or NOT IN a user-specified spacetime region. WORLDPACKS also support real-time hardware-accelerated voxel rendering by exploiting the spatially hierarchical and temporal RLE raster data structure. Our UMWB implementation supports interactive browsing (and offline refinement) of ensembles containing thousands of simulation samples, and fast spatiotemporal queries and ranking. We show UMWB results using a wide variety of physics-based animation phenomena---not just JELL-O ® .

Computer Science↗
DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗
DOE OSTI2023

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗
DOE OSTI2023

TenSQL v.2023.01.20

SAND2024-02520O Tensor SQL Database performs certain relational database management system/structured query language (RDBMS/SQL) queries faster than is possible using existing state-of-the-art databases. Tensor SQL is optimized for sparse linear algebra problems and other whole-table queries. Currently, the database is being used only for research and program development. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Roose, Jonathan↗
DOE OSTI2023

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Coal Power Plant Reinvestment Visualization Tool

This tool, available at https://energycommunities.gov/coal-power-plant-reinvestment-visualization-tool/, serves as a public database and map for the purposes of enabling state and local economic development officials, project developers, and power plant owners to identify and pursue opportunities for plant and community reinvestment. The Coal Power Plant Reinvestment Visualization Tool focuses on coal power plants that have been closed or set-to-retire, alongside key infrastructure characteristics that are relevant for potential redevelopment reinvestment opportunities, including the opportunity to query these data based off pre-defined or user-set queries to identify opportunities for coal power facility reinvestment to support solar, wind, manufacturing, and nuclear. These Data for visualization and query include, but are not limited to: • Electric Transmission Lines • EPA Brownfield Sites (assessed with Federal funding) • Petroleum Terminals • Ports • Railroads • Others The tool will be updated periodically to provide relevant information to enable state and local economic development officials, project developers, and power plant owners to identify and pursue opportunities for economic revitalization and community reinvestment. Within this application data are focused on U.S. power plants with coal generation, where at least one on-site generator utilizes or utilized coal as a fuel source, including planned retirements through 2058 (EIA, 2024). Additional information on energy communities and revitalization opportunities can be accessed on the Interagency Working Group on Coal & Power Plant Communities & Economic Revitalization Energy Communities website (https://energycommunities.gov).

Closures↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
DOE OSTI2021

Document Retrieval and Ranking using Similarity Graph Mean Hitting Times

We present a novel approach to information retrieval and document analysis based on graph analytic methods. Traditional information retrieval methods use a set of terms to define a query that is applied against a document corpus to identify the documents most related to those terms. In contrast, we define a query as a set of documents of interest and apply the query by computing mean hitting times between this set and all other documents on a document similarity graph abstraction of the semantic relationships between all pairs of documents. We present the steps of our approach along with a simple example application illustrating how this approach can be used to find documents related to two or more documents or topics of interest.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

BASIN-3D Data Integration for Selected ARM Data Field Campaign Report

The purpose of this data services request was to demonstrate integration of the Atmospheric Radiation Measurement (ARM) User Facility’s “met” datastreams with time series data from other earth science data sources using the BASIN-3D data synthesis software tool. BASIN-3D is an open-source Python library that enables researchers to integrate data across configured public and private data sources. It provides a common query language for researchers to request measurement locations and time series data based on specified locations, variables, time period, statistics, aggregation, and data quality. BASIN-3D acquires the data that match the query from each configured data source and translates the results into harmonized vocabularies, thus reducing researchers' data-wrangling effort. In addition, because the queries are executed on demand, researchers can easily regenerate their synthesized data sets as new data and/or data updates become available, eliminating one-off data products. BASIN-3D can output data using a variety of different data structures for end-user applications including Python pandas data frames and hdf5 output formats.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗