bstaton1/KuskoHarvData: Manuscript Version
Bibliographic record
Abstract
This release marks a major milestone in package development: submittal of manuscripts and completion of the AYK SSI project that funded its development. (🎉🎉🎉) The package will undoubtedly see future incremental improvements (especially as future years of monitoring data are added, see (vignette("updating-datasets", package = "KuskoHarvData")). However, this release serves as an easy reference to the exact version at this administrative milestone, perhaps for future reproducibility of content in the manuscripts: In-season monitoring of harvest and effort from a large-scale subsistence salmon fishery in western Alaska by B. A. Staton, W. R. Bechtol, L. G. Coggins Jr., G. Decossas, and J. Esquible, submitted to the Canadian Journal of Fisheries and Aquatic Sciences (repository: bstaton1/KuskoHarvEst-ms-analysis; archived under DOI 10.5281/zenodo.10369148). In-season predictions of daily harvest for lower Kuskokwim River subsistence salmon fisheries by B. A. Staton, W. R. Bechtol, L. G. Coggins Jr., and G. Decossas, submitted to the North American Journal of Fisheries Management (repository: bstaton1/KuskoHarvPred-ms-analysis; archived under DOI 10.5281/zenodo.13293677). The package version in this release can be installed via: install.packages("remotes") remotes::install_github("bstaton1/KuskoHarvData", ref = "v2023.5")
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 | 0.012 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.766 | 0.752 |
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".