Chemical Data Evaluation: General Considerations and Approaches for IUPAC Projects and the Chemistry Community
Bibliographic record
Abstract
The International Union of Pure and Applied Chemistry has a long tradition of supporting the compilation of chemical data and their evaluation through direct projects, nomenclature and terminology work, and partnerships with international scientific bodies, government agencies, and other organizations. The IUPAC Interdivisional Subcommittee on Critical Evaluation of Data has been established to provide guidance on issues related to the evaluation of chemical data. In this first report, we define the general principles of the evaluation of scientific data and describe best practices and approaches to data evaluation in chemistry.
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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 | 0.154 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.051 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.023 |
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".