Improvement of Quality in Publication of Experimental Thermophysical Property Data: Challenges, Assessment Tools, Global Implementation, and Online Support
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
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.
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 | 0.001 |
| Open science | 0.001 | 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