Energetic costs of activity in wild lake trout: a calibration study using acceleration transmitters and positional telemetry
Bibliographic record
Abstract
Acceleration telemetry transmitters offer the opportunity to estimate the cost of behaviours in free-ranging fishes, but a methodology to translate acceleration data into metabolic equivalents is still needed. This study extends previous calibration studies, explores how well tail-beat frequency transmitters fulfill their role, and presents a procedure to convert acceleration data into metabolic cost within a framework consistent with traditional fish bioenergetics models and thus facilitates comparisons of energetic costs between natural fish populations. These objectives were achieved by comparing data from lake trout (Salvelinus namaycush) in a laboratory setting with data from three natural populations. In the laboratory, tail-beat frequency, acceleration values, and oxygen consumption increased progressively with swimming speed. In the wild, individual swimming speeds estimated from positional telemetry were consistently underestimated by, but positively related to, transmitter-based acceleration values. The proposed rationale to estimate metabolic rate from acceleration data accounts for variation in fish mass and environmental temperature. We demonstrated how this novel method permits comparison of metabolic costs associated with the levels of activity typical of lake trout living in two different lakes.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".