The IUPAC-NIST Solubility Data Series: A Guide to Preparation and Use of Compilations and Evaluations
Bibliographic record
Abstract
The IUPAC-NIST Solubility Data Series (SDS) is an ongoing project that provides comprehensive reviews of published data for solubilities of gases, liquids and solids in liquids or solids. Data are compiled in a uniform format, evaluated and, where data from independent sources agree sufficiently, recommended values are proposed. This paper is a guide to the SDS and is intended for the benefit of both those who use the SDS as a source of critically evaluated solubility data and who prepare compilations and evaluations for future volumes. A major portion of this paper presents terminology and nomenclature currently recommended by IUPAC and other international bodies and relates these to obsolete forms that appear in the older solubility literature. In addition, this paper presents a detailed guide to the criteria and procedures used in data compilation, evaluation, and presentation and considers special features of solubility in gas+liquid, liquid+liquid, and solid+liquid systems. In the past, much of this information was included in introductory sections of individual volumes of the SDS. However, to eliminate repetitive publication, this information has been collected, updated, and expanded for separate publication here.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.081 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".