Behavioral thermoregulation by maturing adult sockeye salmon (<i>Oncorhynchus nerka</i>) in a stratified lake prior to spawning
Bibliographic record
Abstract
Adult sockeye salmon, Oncorhynchus nerka (Walbaum in Artedi, 1792), return to Lake Washington several months prior to spawning, spending the warmest months of the year in the lake. We proposed that the fish selected a temperature range ideal for final sexual maturation and energy conservation prior to swimming upstream to spawn. The temperature preferences of the adult sockeye salmon in Lake Washington are attributable to physiological factors, as they are not avoiding predators or seeking prey and are not limited by dissolved oxygen. At the Hiram M. Chittenden Locks, 257 sockeye salmon were tagged with temperature loggers in the summer of 2003, and 38 tags with readable data were recovered. The fish spent an average of 6 days swimming through the ship canal's warm water (ca. 18 °C) and then experienced a drop to temperatures of 13 °C or lower when they entered the lake and descended below the thermocline. Fish remained in the lake for an average of 83 days before migrating upstream to spawn, as indicated by a sudden increase in recorded temperature. Approximately 92% of temperature records in the lake were 9–11 °C, corresponding to depths of 18–30 m. The salmon rarely occupied the cooler and warmer waters available to them. Finally, the apparent thermal preference decreased over the summer, perhaps as a function of sexual maturation.
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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) | 0.001 | 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".