Use of the Kalman filter to reconstruct historical trends in productivity of Bristol Bay sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
Fisheries scientists and managers are concerned about potential long-term, persistent changes in productivity of fish stocks that might result from future climatic changes or other alterations in aquatic systems. However, because of large natural variability and measurement error in fisheries data, such changes are usually difficult to detect until long after they occur. Previous research using numerous Monte Carlo simulation trials showed that a Kalman filter performed better than standard estimation techniques in detecting such trends in a timely manner. Therefore, we used historical data along with a Kalman filter that included a time-varying Ricker a parameter to reconstruct changes in productivity (recruits per spawner at a given spawner abundance) of eight Bristol Bay, Alaska, sockeye salmon (Oncorhynchus nerka) stocks over the past 40 years. Productivity generally increased for most stocks but varied widely for others and dramatically decreased in another. Such large changes in productivity are important for management. They greatly affected optimal spawner abundances and optimal exploitation rates, suggesting that in the future, scientists should consider using models with time-varying productivity parameters.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 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 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".