Bibliographic record
Abstract
This dataset is a version of the New Salmon Escapement Database (NuSEDS) downloaded from Open Data Canada on January 21, 2025 at https://open.canada.ca/data/en/dataset/c48669a3-045b-400d-b730-48aafe8c5ee6. These data have been cleaned and matched to Conservation Units (CUs) by staff at the Pacific Salmon Foundation. Details of the data cleaning procedure are outlined in the Pacific Salmon Explorer Technical Report (Appendix 2) available online at https://www.salmonexplorer.ca/methods/appendix-2.html. The entire cleaning procedure is available online at a_nuseds_collation and corresponding code a_nuseds_collation.Rmd. The procedure to match the cleaned data to CUs is available online at b_nuseds_cuid_pse and corresponding code b_nuseds_cuid_pse.Rmd. This revised dataset (v3, 2025-04-15) includes observed counts of zero. This is the dataset used in Atkinson et al. 2025. Monitoring for fisheries or for fish? Declines in monitoring of salmon spawners continue despite a conservation crisis, published in the CJFAS. The definition of the fields/columns is available at: METADATA_2_nuseds_cuid_streamid.csv
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.062 |
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".