Modeling the Influence of Environmental Factors on Spawning Migration Mortality for Sockeye Salmon Fisheries Management in the Fraser River, British Columbia
Bibliographic record
Abstract
Abstract The impact of freshwater environmental factors on spawning migration mortality was modeled to provide a predictive tool for fisheries management of four run timing groups of Fraser River sockeye salmon Oncorhynchus nerka: early Stuart (Stuart Lake), early summer, summer, and late. We tested the significance of different measures of water temperature, discharge, fish abundance, and entry timing for forecasting discrepancies between lower‐river and upriver escapement estimates using multiple regressions of principal component scores. Descriptive discrepancy models (i.e., “management adjustment” models) identified using Akaike's information criterion were consistent with the known biology of each group. For example, temperature and discharge thresholds were selected for early Stuart run discrepancy models, reflecting the extremes in both variables experienced by these early migrants. Predictive discrepancy models were also generated for each run timing group by using the limited number of environmental variables that are available in‐season to fisheries managers. Even predictive discrepancy models using simple environmental metrics of average river temperature, flow, and river entry timing provide a valuable tool for forecasting relative indices of spawning migration mortality. This study provides an example of how environmentally based predictive tools can be used to inform fisheries management decisions and improve the probability of achieving spawning escapement targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".