Reproducible Results Policy
Bibliographic record
Abstract
To further improve the reproducibility of work published in the Journal of Water Resources Planning and Management, narrow the gap between research and practice, and promote reproducibility as a moral and ethical imperative in our practice of science and engineering, the editorial board now encourages authors to add a "Reproducible Results" section immediately after the "Data Availability Statement" section in their manuscript.To incentivize authors to make their results more reproducible, the Journal will publish technical papers and case studies with verified reproducible results open access free to the authors for the next year.In future years, the Journal will publish technical papers and case studies with verified reproducible results open access either free or for a reduced fee, as funds are available.The Journal will also recognize papers with reproducible results in a new special collection and offer two new annual reproducibility awards for authors and the people who assess the reproducibility of results.
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.
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 | 0.391 | 0.651 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.039 | 0.032 |
| Open science | 0.015 | 0.017 |
| Research integrity | 0.035 | 0.023 |
| Insufficient payload (model declined to judge) | 0.146 | 0.105 |
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".