Calibration of accelerometer transmitters to enable estimation of field metabolic rates in walleye
Bibliographic record
Abstract
Bioenergetic modelling is valuable for addressing many questions in animal ecology. However, applying these models to wild animals is limited by challenges with collecting in situ energetics data. To enable field applications, we conducted laboratory experiments using a swim tunnel respirometer on wild ( n = 28) and hatchery-origin ( n = 19) walleye ( Sander vitreus) to calibrate acoustic accelerometer transmitters (InnovaSea V13A and V16AT) for estimating metabolic rate ( Ṁ O2 ). Walleye (0.36–3.06 kg) underwent ramp- U c rit swim trials ( n = 70) across four temperatures (5–21°C). Using mixed effects models, we analyzed critical swimming speed ( U crit ), swimming speed, tailbeat frequency, and Ṁ O2 as functions of body mass, acceleration, sex, and water temperature. Ṁ O2 decreased with body mass and increased with higher swimming speeds, acceleration values, and water temperatures. Notably, Ṁ O2 increased more rapidly with acceleration at higher temperatures. No mass-specific sex differences were observed across measured parameters, and there were no differences in U crit or Ṁ O2 between control and tagged fish. These findings support the use of accelerometers to generate field estimates of energy expenditure in wild walleye.
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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) | 0.001 | 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".