Density, climate, and the processes of prespawning mortality and egg retention in Pacific salmon (<i>Oncorhynchus</i>spp.)
Bibliographic record
Abstract
In 2004 and 2005, exceptionally large runs of sockeye salmon (Oncorhynchus nerka) to the Alagnak River system in Bristol Bay, Alaska, coincided with weak runs to the nearby Kvichak River system. Restricted fishing to protect the Kvichak populations resulted in densities on the Alagnak River system's spawning grounds that were 11.5-fold (in 2004) and 9.0-fold (in 2005) above the long-term (1956–2003) average. Carcass sampling indicated that 23% (2004) and 44% (2005) of the potential egg deposition was lost to prespawning mortality or incomplete spawning in the Alagnak populations. Much lower levels of egg retentions were observed in spawning populations in the Kvichak River and Wood River systems, where the runs did not appreciably exceed the escapement goals, indicating that density-dependent spawning failure may have occurred. However, in 2005, significantly higher egg retention rates were observed in the Alagnak River system despite slightly lower densities than in 2004, indicating that environmental processes (probably low river levels and high temperatures) influenced prespawning mortality as well. More limited sampling in 2006 revealed only 3% egg retention in one of the Alagnak populations, but the combination of lower density and cooler conditions did not allow us to determine the relative contributions of these two factors to spawning failure.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".