Assessing the potential of acoustic telemetry to underpin the regional management of basking sharks (Cetorhinus maximus)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Acoustic telemetry can provide valuable space-use data for a range of marine species. Yet the deployment of species-specific arrays over vast areas to gather data on highly migratory vertebrates poses formidable challenges, often rendering it impractical. To address this issue, we pioneered the use of acoustic telemetry on basking sharks ( Cetorhinus maximus ) to test the feasibility of using broadscale, multi-project acoustic receiver arrays to track the movements of this species of high conservation concern through the coastal waters of Ireland, Northern Ireland, and Scotland. Throughout 2021 and 2022, we tagged 35 basking sharks with acoustic transmitters off the west coast of Ireland; 27 of these were detected by 96 receiver stations throughout the study area ( n = 9 arrays) with up to 216 detections of an individual shark (mean = 84, s.d. 65). On average, sharks spent ~ 1 day at each acoustic array, with discrete residency periods of up to nine days. Twenty-one sharks were detected at multiple arrays with evidence of inter-annual site fidelity, with the same individuals returning to the same locations in Ireland and Scotland over 2 years. Eight pairs of sharks were detected within 24 h of each other at consecutive arrays, suggesting some level of social coordination and synchronised movement. These findings demonstrate how multi-project acoustic telemetry can support international, cost-effective monitoring of basking sharks and other highly mobile species. Decision support tools such as these can consolidate cross-border management strategies, but to achieve this goal, collaborative efforts across jurisdictions are necessary to establish the required infrastructure and secure ongoing support.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it