Field trials of a new physiological data logger in active fishes
Bibliographic record
Abstract
We have designed and constructed implantable data loggers that measure heart rate (HR) and body temperature of free‐ranging animals. We present data from the first field trials of these loggers in penned southern bluefin tuna (Thunnus maccoyii) under aquacultural conditions, and in spawning sockeye salmon (Oncorhynchus nerka) under natural conditions. Bluefin tuna (~ 15.2kg) maintained visceral temperature (VT) between 0.4 – 2.6°C above ambient water temperature (18.5°C) during non‐digesting periods, while HR ranged from 33 – 74 beats/min. VT rose to 1.4 – 6.5°C above ambient at the peak of the digestive period, and the magnitude of this increase was dependent on ration size. HR followed a similar trend during the digestive period, reaching levels of 52 – 117 beats/min and implying that the postprandial increase in oxygen consumption is assisted largely by an increase in HR. The duration of digestion ranged from 10.7 to 30.9 h depending on ration size. Sockeye salmon were implanted with data loggers in October 2007 when they had migrated from the ocean to their natal stream to spawn and subsequently die. Data analyses are ongoing, but it has been confirmed that salmon carrying data loggers do not have a reduced lifetime on the spawning ground, implanted salmon display normal courting and mating behaviours, and implanted salmon completed their lifecycle normally by spawning before death.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".