Monitoring fish behaviour with a remote, combined acoustic/radio biotelemetry system
Bibliographic record
Abstract
Biotelemetry is a powerful instrument for monitoring aquatic species in their natural environment. Using telemetry, animals can be monitored from a passive perspective, without the biases associated with conventional handling and sampling techniques. To monitor aquatic species in remote environments, with vast stretches of water, and in situations requiring both acoustic and radio transmissions (e.g. for diadromous fish), advances in telemetry are necessary. In this paper, a field-proven telemetry system based on a radio receiver and incorporating combined acoustic and radio smart transmitters, wireless hydrophones and two-way satellite communications is described. The system was first deployed in Bay d’Espoir, Newfoundland, Canada, in 1998. The purpose of this deployment was to determine whether aquaculture triploid steelhead trout (Oncorhynchus mykiss Walbaum) (1.5–2.0 kg), experimentally released in the vicinity of a commercial aquaculture site, remained at the site (site fidelity) or dispersed. Two sets of fish releases, summer and winter, were performed to determine seasonal effects on the movement of aquaculture triploid steelhead trout in the wild. The results suggested strong site fidelity among steelhead trout when released during the growing season (summer). However, less fidelity was displayed for the winter released steelhead.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".