The IUPAC-NIST Solubility Data Series: A guide to preparation and use of compilations and evaluations (IUPAC Technical Report)
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it