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Record W4315649523 · doi:10.1515/pac-2021-1002

Reference materials for phase equilibrium studies. 2. Solid–liquid equilibria (IUPAC Technical Report)

2022· article· en· W4315649523 on OpenAlexaff
Ala Bazyleva, William E. Acree, Vladimir Diky, Glenn Hefter, Johan Jacquemin, M. Clara F. Magalhães, Joseph W. Magee, D. Kirk Nordstrom, John P. O’Connell, James D. Olson, Ilya Polishuk, Kurt A. G. Schmidt, John M. Shaw, J. P. Martin Trusler, R. D. Weir

Bibliographic record

VenuePure and Applied Chemistry · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsRoyal Military College of CanadaUniversity of Alberta
FundersCentro de Investigação em Materiais Cerâmicos e CompósitosFundação para a Ciência e a TecnologiaMinisterio de Economía y CompetitividadInternational Union of Pure and Applied Chemistry
KeywordsChemical nomenclatureChemistrySolubilityAqueous solutionThermodynamicsPhase equilibriumPhase (matter)Binary numberLiquid phaseConsistency (knowledge bases)Process engineeringOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Abstract This article is the second of three projected IUPAC Technical Reports on reference materials for phase equilibrium studies. The goal of this project was to select reference systems with critically evaluated property values for the verification of instruments and techniques used in phase equilibrium studies of mixtures. This report proposes seven systems for solid–liquid equilibrium studies, covering the four most common categories of binary mixtures: aqueous systems with organic solutes, aqueous systems with inorganic solutes, non-aqueous systems, and systems with low solubility. For each system, the available literature sources, accepted data, smoothing equations, and estimated uncertainties are given.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.122
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.007
Science and technology studies0.0020.000
Scholarly communication0.0010.003
Open science0.0040.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1220.102

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 head0.033
GPT teacher head0.315
Teacher spread0.282 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2022
Admission routes1
Has abstractyes

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