Modeling spatiotemporal trends in the productivity of North Pacific Salmon
Bibliographic record
Abstract
Fisheries management of North Pacific salmon stocks greatly relies on the understanding of changes in spawning and survival over time and across habitats. Underlying the yearly observed number of surviving salmon is a productivity parameter that cannot be directly measured and, moreover, is masked by short‐term changes in the observed population. We model the unobserved productivity of chum, sockeye, and two broodlines of pink salmon along the Pacific Coast of North America as a smoothly varying function of time and spatial location based on the Ricker spawner–recruit model of salmon reproduction. The candidate models belong to the class of Gaussian additive models and require the selection of smoothing parameters that control the trade‐off between fit to the data and smoothness of the estimated functions. We select the smoothing parameters by optimizing the pseudo Bayes information criterion, which incorporates prior knowledge about the degree of smoothness of the estimated functions and is well suited for detecting low‐frequency oscillations in the data, such as those due to long‐term climate effects. Comparing the candidate models based on fit and model parsimony via the Akaike information criterion, we find that the productivity components of time and spatial location may be related nonlinearly. We find evidence of an increase in productivity in the mid‐1970s for chum and sockeye populations and a north–south inverse relationship in productivity among sockeye and odd‐year pink salmon stocks. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".