Estimating Indices of Abundance and Escapement of Pacific Salmon for Data‐Limited Situations
Bibliographic record
Abstract
Abstract We demonstrate the process of synchronously combining multiple sources of available fishery information to estimate total abundance in data‐limited situations. The application is specific to semelparous populations, such as Pacific salmon, where only data for spawners and recruits are necessary to describe the dynamics of these populations. We apply this technique to summer chum salmon Oncorhynchus keta of the Kuskokwim and Yukon rivers of Alaska. Since 1997, low numbers of returning chum salmon to these rivers have resulted in low harvests, with significant negative economic and social impacts to rural residents of the region. The existing programs for assessing salmon stock in these river basins are inadequate for conventional estimates of total run abundance and the modeling of stock dynamics necessary to derive a quantitative assessment of the returns. Our approach was to utilize the pattern extraction qualities of principal components analysis (PCA) to estimate the underlying trend in escapement. We then combined this index with other available fishery data in a maximum likelihood statistical framework, weighting the data sets according to their quality. Using this methodology, we derived indices of chum salmon abundance and escapement for the Kuskokwim and Yukon rivers. Data sources included commercial catch and effort, escapement surveys, test fishery catch rates, and whole‐river sonar counts. Error estimates of the time series of abundance and escapement as well as of the model parameters were generated by bootstrap methods. We found that several parameters of the model were confounded without some independent measure of total abundance or escapement. We also determined that the escapement trend estimated by PCA was consistent over a large geographic area, suggesting that survival was predominantly influenced by conditions where the fish share a common environment. We suggest that our methodology may be appropriate to other regions and different semelparous species.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".