Thermal effects on behavior of juvenile walleye pollock (<i>Theragra chalcogramma</i>): implications for energetics and food web models
Bibliographic record
Abstract
The behavioral responses of fishes to temperature variation have received little attention despite their direct implications to bioenergetics-based models of production and encounter-based models of food web dynamics. Behavioral characteristics of juvenile walleye pollock (Theragra chalcogramma), a pelagic marine zooplanktivore, were examined between 2 and 9 °C in large arenas. Routine swim speed, path sinuosity, and schooling cohesiveness of fish in small groups were described from overhead video observations. In a separate experiment, maximum swimming speeds were measured in a recirculating flume. Routine and maximum swimming speeds had contrasting responses to temperature demonstrating a behavioral rather than physiological regulation of activity level. Routine swim speed was 48% faster at 2 °C than at 9 °C. This result is inconsistent with the assumption of a constant activity multiplier for metabolism incorporated into most bioenergetics models of fish growth. Increased swim speed, along with the reduced path sinuosity observed at low temperatures, may reflect kinetic aspects of habitat selection. Group cohesion increased at low temperatures, with nearest neighbors averaging 32% closer at 2 °C than at 9 °C. These results demonstrate that representative models of energy flow through marine food webs depend on an improved understanding of the behavioral as well as physiological responses of fishes to thermal variation.
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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".