Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future
Bibliographic record
Abstract
Abstract.—It was not too long ago that our best available science advised that Pacific salmon abundance could be rebuilt to historic levels by adding more fish to the ocean. In Canada it was proposed that an enhancement program would not only benefit Pacific salmon, but it would also be repaid when the increased abundances were fished and taxed. The belief that there was unused carrying capacity in the ocean that could be filled with hatchery-reared fish persisted into the 1990s as indicated by plans to rebuild coho salmon, Oncorhynchus kisutch, stocks. It was in the 1990s that most researchers accepted that Pacific salmon production was related to trends in ocean carrying capacity. It was also accepted that regimes were real, resulting in persistent states in carrying capacity that shifted quickly to new states on a decadal scale. It was unsettling that climate trends could be directly related to Pacific salmon production because fisheries management science at the time did not include climate as a major factor that caused trends in production. The recognition that climate was a major factor regulating salmon productivity was also alarming since most scientists believed that humans were rapidly changing the climate. An additional concern was that hatchery-reared Pacific salmon were now common throughout the distribution of Pacific salmon and it was uncertain how the ability of Pacific salmon to adapt to climate variability had been compromised by the intermixing of hatchery and wild fish. The days of blaming everything on overfishing are gone. Providing the best available scientific advice now requires maneuvering through the uncharted waters of climate change with a science that has lost some of its steerage. The solution may be something we have known for years. Fisheries management science must improve forecasts. Model forecasting on a large scale may improve greatly as we discover the planetary forces that shift climate regimes and alter the trends in ocean carrying capacity for Pacific salmon. Model forecasting on a regional scale will also improve as the linkages between climate and marine survival are discovered. Fisheries scientists need to form teams that include biologists, oceanographers, climatologists, and perhaps physicists. Science organizations that find ways to establish, recognize, and reward these teams will probably provide the best management advice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".