Locomotory activity and depth distribution of adult great barracuda (Sphyraena barracuda) in Bahamian coastal habitats determined using acceleration and pressure biotelemetry transmitters
Bibliographic record
Abstract
Documenting free-swimming fish in their natural environment using acoustic transmitters equipped with acceleration and pressure sensors may contribute to knowledge of locomotory behaviour for a variety of aquatic species. Previously, collection of acceleration data has been limited to archival loggers, necessitating retrieval of the devices; however, recent advances in biotelemetry have allowed for acceleration data to be transmitted to a remote receiver. To illustrate the application of this technology, relative locomotory activity and depth utilisation of adult great barracuda (Sphyraena barracuda) were monitored across habitat types and diel periods using acoustic transmitters equipped with tri-axial acceleration and pressure sensors within an acoustic telemetry array (n = 53 receivers) deployed in The Bahamas. Although there were no differences in acceleration or depth use across habitats or diel periods, there was evidence of movement into shelf habitat during mid-day where they occupied depths >10 m. Given both the method of calculating the accelerometer output, and that the transmitters were unable to store and transmit large quantities of data, we suggest choosing transmitter settings with a short average delay and high transmission frequency to optimise data quality and resolution. This paper represents one of the first reports of the use of telemetered acceleration values from free-swimming fish.
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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 | 0.000 |
| 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".