Observations of in situ Atlantic bluefin tuna (Thunnus thynnus) with 500-kHz multibeam sonar
Bibliographic record
Abstract
Abstract Field studies to investigate the survey capability of a 500-kHz multibeam sonar to monitor and quantify Atlantic bluefin tuna were undertaken at several fishing locations (commercial and recreational) off Prince Edward Island and at a grow-out pen in Nova Scotia. The results demonstrate that individual bluefin can be detected, enumerated, and tracked acoustically within the swathe of the sonar. Bluefin were observed individually, and in groups ranging from 2 to 16 fish. Schooling tuna, assumed to be foraging, were observed moving in a soldier formation. Estimates of in situ swimming speeds (0.5–11.0 m s−1) were made by tracking individual fish within the swathe. The mean interspatial distance between fish when swimming in a group of two or more tuna was estimated to be 8.94 m with a range of 2.68–22.63 m. Groups of up to 48 bluefin were observed aggregating around active herring gillnet vessels. Dorsal aspect target strength estimates of bluefin, obtained from an accompanying 120-kHz echosounder (Simrad EK60), ranged from −33 to −14 dB for fish from 220 to 313 cm (size estimated from commercial catches). Sonar detection ranges were dependent upon sea state and water depth. In rough seas, the surface layer became too turbulent, and air bubble attenuation too high, to consistently separate reverberation from fish-like targets. In shallow water (20–30 m), a range setting of >50 m could not be utilized due to seabed reflections. In water depths >50–60 m, a tilt angle of 7.5o below the horizontal allowed the sonar's range of up to 150 m to be utilized with minimal reverberation from the surface and seabed. The results indicate there is potential for using a 500-kHz multibeam sonar in fishery-independent surveys to monitor and to quantify bluefin in shallow water (<100 m).
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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 | 0.000 | 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".