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Record W4392750757 · doi:10.5334/jors.403

BEaTmap: Simplified Rigorous BET Analysis of Isothermal Adsorption Data

2024· article· en· W4392750757 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Open Research Software · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsMcGill UniversityUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceIsothermal processAdsorptionThermodynamicsPhysical chemistryChemistryPhysics

Abstract

fetched live from OpenAlex

BEaTmap is an open-source software designed to improve the analysis of isothermal adsorption data. The specific surface area of a porous material is commonly obtained through applying the classic BET theory to the adsorption of inert gases and vapors in the relative pressure range of 5% to 35% and presented as a single value. However, this cookie-cutter approach can yield thermodynamically inconsistent results that are incorrect or misleading. BEaTmap provides a conceptual tool for analyzing the entire set of isothermal adsorption data with BET theory and presents the range of all possible values that are thermodynamically and mathematically consistent. The analysis is presented as a heatmap indicating the results for all valid relative pressure ranges, offering the user a more comprehensive specific surface area answer. The code is written in Python and is available as both a web app and Python package. BEaTmap, documentation, and examples are freely available on GitHub.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.194
GPT teacher head0.488
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it