Exploring relationships between oxygen consumption and biologger‐derived estimates of heart rate in two warmwater piscivores
Bibliographic record
Abstract
Abstract Estimating metabolic rate in wild, free‐swimming fish is inherently challenging. Here, we explored using surgically implanted heart rate biologgers to estimate metabolic rate in two warmwater piscivores, bowfin Amia calva (Linneaus 1766) and largemouth bass Micropterus salmoides (Lacepède 1802). Fish were surgically implanted with heart rate loggers, allowed to recover for 24 h, exposed to a netting and air exposure challenge, and then placed into respirometry chambers so that oxygen consumption rate (ṀO2) could be measured in parallel to heart rate (fH) for a minimum of 20 h (ca. 20 estimates of ṀO2). Heart rate across the duration of the experiment (at 19°C) was significantly higher in largemouth bass (mean ± s.d., 45 ± 14 beats min−1, range 18–86) than in bowfin (27 ± 9 bpm, range 16–98). Standard metabolic rate was also higher in largemouth bass (1.06 ± 0.19 mg O2 kg−1 min−1, range 0.46–1.36) than in bowfin (0.89 ± 0.17 mg O2 kg−1 min−1, range 0.61–1.28). There were weak relationships between fH and ṀO2, with heart rate predicting 28% of the variation in oxygen consumption in bowfin and 23% in largemouth bass. The shape of the relationship differed somewhat between the two species, which is perhaps unsurprising given their profound differences in physiology and life history, illustrating the need to carry out species‐specific validations. Both species showed some potential for a role of fH in efforts to estimate field metabolic rates, although further validation experiments with a wider range of conditions (e.g., digestive states, swimming activity) would likely help improve the strength of the ṀO2–fH relationship for use in field applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".