Using heart rate and acceleration biologgers to estimate winter activity costs in free-swimming largemouth bass
Bibliographic record
Abstract
Winter is a critical period for largemouth bass (Micropterus nigricans) with winter severity and duration limiting their population growth at northern latitudes. Unfortunately, we have an incomplete understanding of their winter behaviour and energy use in the wild. More winter-focused research is needed to better understand their annual energy budget, improve bioenergetics models, and establish baselines to assess the impacts of climate warming; however, winter research is challenging due to ice cover. Implantable tags show promise for winter-focused research as they can be deployed prior to ice formation. Here, using swim tunnel respirometry, we calibrated heart rate and acceleration biologgers to enable estimations of metabolic rate (ṀO2) and swimming speed in free-swimming largemouth bass across a range of winter-relevant temperatures. In addition, we assessed their aerobic and swim performance. Calculated group thermal sensitivities of most performance metrics indicated the passive physicochemical effects of temperature, suggesting little compensation in the cold; however, resting metabolic rate and critical swimming speed showed partial compensation. We found strong relationships between acceleration and swimming speed, as well as between ṀO2 and heart rate, acceleration, or swimming speed. Jackknife validations indicated that these modeled relationships accurately estimate swimming speed and ṀO2 from biologger recordings. However, there were relatively few reliable heart rate recordings to model the ṀO2 relationship. Recordings of heart rate were high-quality during holding but dropped during experimentation, potentially due to interference from aerobic muscles during swimming. The models informed by acceleration or swimming speed appear to be best suited for field applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".