Calibrating acceleration transmitters to quantify the seasonal energetic costs of activity in lake trout
Bibliographic record
Abstract
Abstract Bioenergetics models are powerful tools used to address a range of questions in fish biology. However, these models are rarely informed by free‐swimming activity data, introducing error. To quantify the costs of activity in free‐swimming fish, calibrations produced from standardized laboratory trials can be applied to estimate energy expenditure from sensor data for specific tags and species. Using swim tunnel respirometry, we calibrated acceleration sensor‐equipped transmitting tags to estimate the aerobic metabolic rates ( Ṁ O 2 ) of lake trout ( Salvelinus namaycush ) at three environmentally relevant temperatures. Aerobic and swim performance were also assessed. Like other calibrations, we found strong relationships between Ṁ O 2 and acceleration or swimming speed, and jackknife validations and data simulations suggest that our models accurately predict metabolic costs of activity in adult lake trout (~5% algebraic error and ~20% absolute error). Aerobic and swim performance metrics were similar to those reported in other studies, but their critical swimming speed was lower than expected. Additionally, lake trout exhibited a wide aerobic scope, suggesting that the avoidance of waters ≥15°C may be related to selection for optimal growing temperatures. The ability to quantify the free‐swimming energetic costs of activity will advance our understanding of lake trout ecology and may yield improvements to bioenergetics model.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".