Improvement of Quality in Publication of Experimental Thermophysical Property Data: Challenges, Assessment Tools, Global Implementation, and Online Support
Bibliographic record
Abstract
This article describes a 10-year cooperative effort between the U.S. National Institute of Standards and Technology (NIST) and five major journals in the field of thermophysical and thermochemical properties to improve the quality of published reports of experimental data. The journals are Journal of Chemical and Engineering Data, The Journal of Chemical Thermodynamics, Fluid Phase Equilibria, Thermochimica Acta, and International Journal of Thermophysics . The history of this unique cooperation is outlined, together with an overview of software tools and procedures that have been developed and implemented to aid authors, editors, and reviewers at all stages of the publication process, including experiment planning. Both successes and failures are highlighted. The procedures are now well established and are designed to yield maximum benefit to all stakeholders (authors, editors, reviewers, publishers, readers, data users, etc.) through the establishment of procedures and support tools that efficiently serve the specific interests of those involved. All specially designed tools and procedures are described fully, together with their benefits and examples of application. A key feature of the cooperation is the efficient validation of experimental data after peer review but before acceptance for publication. Nearly 1000 articles per year are considered within the scope of this work, with significant problems identified in roughly one-third of these. Full statistics for the findings are given, and a variety of examples of common problems found are given.
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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 | 0.547 | 0.764 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.043 | 0.044 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".