Comparative thermal biology and depth distribution of largemouth bass (<i>Micropterus salmoides</i>) and northern pike (<i>Esox lucius</i>) in an urban harbour of the Laurentian Great Lakes
Bibliographic record
Abstract
Understanding how individuals are distributed in space and time, as well as how they interact with dynamic environmental conditions, represent fundamental knowledge gaps for many fish species. Using acoustic telemetry tags, we monitored the temperatures and depths used by northern pike (Esox lucius L., 1758) and largemouth bass (Micropterus salmoides (Lacepède, 1802)) in Toronto Harbour (Lake Ontario). Northern pike and largemouth bass had similar thermal experiences throughout the year, except during summer, when northern pike were observed in cooler waters than largemouth bass. Both species used different depths throughout the year, with northern pike occupying deeper depths. Statistical modelling indicated that depth usage was influenced by all variables (season, species, and body size) and interactions between them, whereas thermal preferences were influenced by the main effects and interactions between species:season and species : body size. Both species were observed at temperatures warmer than those in the vicinity of nearby telemetry stations, but as station temperatures exceeded 20 °C, northern pike moved into cooler water, indicating active thermoregulation. These data will be useful for refining our understanding of the spatial ecology of fish and for informing fisheries and habitat management in this and other urban harbours of the Laurentian Great Lakes.
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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".