Accelerometer‐based swimming metabolism of smallmouth bass (<i>Micropterus dolomieu</i>)
Bibliographic record
Abstract
Abstract Bioenergetics is informative for a range of fundamental and applied resource management questions, but findings are often constrained by a lack of ecological realism due to the challenges of remotely estimating key parameters such as metabolic rate. To enable field applications, we conducted a calibration study with smallmouth bass (Micropterus dolomieu, 0.7–2 kg) surgically implanted with accelerometer transmitters and exposed to a ramp‐Ucrit swimming protocol in a swim tunnel respirometer across a range of water temperatures (6, 12, 18, and 24°C). There was an exponential increase in fish acceleration with swimming speed, and acceleration per speed was higher in smaller fish and female fish, and at colder temperatures. Mass‐specific fish metabolic rate (MO2; mg O2 kg−1 h−1) increased with swimming speed, acceleration, and temperature, and decreased with fish mass, which when combined were strong predictors of MO2. Maximum metabolic rate (MMR) was estimated to peak at 22°C, but maximum sustained swimming speed (Ucrit) remained high at c. 90–100 m s−1 above 20°C, based on second‐order polynomial functions. Aerobic scope (AS) estimates peaked at 20°C (>90% AS at 17–24°C; >50% AS at 11–28°C). Males exhibited marginally higher MMR, AS, and Ucrit than females at higher temperatures. Larger fish generally exhibited higher Ucrit, but smaller fish had a marginally broader performance range (AS, Ucrit) among temperatures, benefiting from higher MMR despite a steeper increase in resting metabolic rate with temperature. These findings enable field studies to estimate metabolic metrics of smallmouth bass in situ to characterize their ecological energetics and inform bioenergetics models.
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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".