Temporal plasticity in thermal‐habitat selection of burbot <i>Lota lota</i> a diel‐migrating winter‐specialist
Bibliographic record
Abstract
In this study, animal-borne telemetry with temperature sensors was coupled with extensive habitat temperature monitoring in a dimictic reservoir, to test the following hypotheses: behavioural thermoregulation occurs throughout the year and temperature selection varies on a diel and seasonal basis, in a winter-specialist diel-migrating fish. Burbot Lota lota demonstrated nightly behavioural thermoregulation throughout the year, with a large seasonal shift between selection for very cold temperatures (<2° C) optimal for reproduction during the spawning period and selection for warmer temperatures (12-14° C) optimal for hunting and feeding during non-reproductive periods. During daylight hours, while L. lota avoided habitats warmer than optimal for reproduction and feeding during the spawning and non-reproductive periods, respectively, active selection was limited to selection for 4-6° C habitat during the prespawning period. Although behavioural thermoregulation explained the night-time migration, behavioural thermoregulation only partially explained daytime behaviour, indicating that diel migration is best explained by a combination of factors. Thus, thermal-habitat selection was a good predictor of night-time habitat occupancy in a diel-migrating species. Together, these results show that thermal-habitat selection by fishes may be important throughout the year and a more seasonally plastic behaviour than previously recognized.
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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".