Historical trends in productivity of 120 Pacific pink, chum, and sockeye salmon stocks reconstructed by using a Kalman filter
Bibliographic record
Abstract
Temporal trends in productivity of Pacific salmon ( Oncorhynchus spp.) stocks are important to detect in a timely and reliable manner to permit appropriate management responses. However, detecting such trends is difficult because observation error and natural variability in survival rates tend to obscure underlying trends. A Kalman filter estimation procedure has previously been shown to be effective in such situations. We used it on a Ricker spawner–recruit model to reconstruct indices of annual productivity (recruits per spawner (R/S) at low spawner abundance) based on historical data for 120 stocks of pink ( Oncorhynchus gorbuscha ), chum ( Oncorhynchus keta ), and sockeye ( Oncorhynchus nerka ) salmon. These stocks were from Washington, British Columbia, and Alaska. The resulting estimated temporal trends in productivity show large changes (on average 60%–70% differences in R/S and average ratios of highest to lowest R/S between 5.4 and 7.9 for the three species). Such changes suggest that salmon stock assessment methods should take into account possible nonstationarity. This step will help provide scientific advice to help managers to meet conservation and management objectives. The Kalman filter results also identified some stocks that did not share temporal trends with other stocks; these exceptions may require special monitoring and management efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".