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Record W4232770983 · doi:10.1515/iupac.83.0517

Extension of ThermoML: The IUPAC Standard for Thermodynamic Data Communications

2016· dataset· en· W4232770983 on OpenAlex
Michael Frenkel, Robert D. Chirico, Vladimir Diky, Paul L. Brown, J. H. Dymond, Robert N. Goldberg, Anthony R. H. Goodwin, Heiko Heerklotz, Erich Königsberger, John E. Ladbury, Kenneth N. Marsh, David P. Remeta, Stephen E. Stein, W. A. Wakeham, Peter A. Williams

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

VenueIUPAC Standards Online · 2016
Typedataset
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemical nomenclatureComputer scienceExtension (predicate logic)Schema (genetic algorithms)XMLData exchangeInformation retrievalDatabaseData miningTheoretical computer scienceThermodynamicsChemistryProgramming languagePhysicsWorld Wide Web

Abstract

fetched live from OpenAlex

ThermoML is an XML-based approach for storage and exchange of experimental, predicted, and critically evaluated thermophysical and thermochemical property data. Extensions to the ThermoML schema for the representation of speciation, complex equilibria, and properties of biomaterials are described. The texts of 14 data files illustrating the new extensions are provided as Supplementary Information together with the complete text of the updated ThermoML schema.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0090.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.449
Teacher spread0.407 · 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