Accelerometry estimates field metabolic rate in giant Australian cuttlefish Sepia apama during breeding
Bibliographic record
Abstract
1. Estimating the metabolic rate of animals in nature is central to understanding the physiological, behavioural and evolutionary ecology of animals. Doubly labelled water and heart-rate methods are the most commonly used approaches, but both have limitations that preclude their application to some systems. 2. Accelerometry has emerged as a powerful tool for estimating energy expenditure in a range of animals, but is yet to be used to estimate field metabolic rate in aquatic taxa. We combined two-dimensional accelerometry and swim-tunnel respirometry to estimate patterns of energy expenditure in giant Australian cuttlefish Sepia apama during breeding. 3. Both oxygen consumption rate (Vo2) and swimming speed showed strong positive associations with body acceleration, with coefficients of determination comparable to those using similar accelerometers on terrestrial vertebrates. Despite increased activity during the day, field metabolic rate rarely approached Vo2, and night-time Vo2 was similar to that at rest. 4. These results are consistent with the life-history strategy of this species, which has a poor capacity to exercise anaerobically, and a mating strategy that is visually based. With the logistical difficulties associated with observation in aquatic environments, accelerometry is likely to prove a valuable tool for estimating energy expenditure in aquatic animals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".