Patterns of gametic investment by female stream‐and ocean‐type chinook salmon
Bibliographic record
Abstract
Investment in fecundity and egg size is compared among two types of chinook salmonOncorhynchus tshawytscha: stream‐type, which undertake long, arduous oceanic and upstream migrations and often return to their natal rivers (where they do not feed)months before spawning, and ocean‐type, which undertake relatively short oceanic and upstream migrations and remain at sea feeding and accumulating energy until a few days or weeks before spawning. The coefficient of variation in egg size for both life‐history types was significantly less than variation in fecundity, reflecting the expected strong selection on egg size relative to egg number. Total investment in the gonad varied significantly among years in both life‐history types, apparently reflecting variation in oceanic feeding conditions. Stream‐type chinook were smaller in total body size and more than 1 year older than ocean‐type. Stream‐type also invested relatively less of their total body energy in eggs and produced smaller eggs than ocean‐type, reflecting the greater energetic cost of their longer migration and freshwater maintenance. These differences suggest that stream‐type chinook pay a considerable fitness penalty in reproductive output. This may be offset by the fact that stream‐type go to sea at larger size and migrate far offshore, thereby avoiding some of the mortality costs of oceanic migration.
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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 | 0.000 |
| 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) | 0.002 | 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".