Up in the air: drone images reveal underestimation of entanglement rates in large rorqual whales
Bibliographic record
Abstract
Entanglement in fishing gear is a significant threat to many cetaceans. For the 2 largest species, the blue whale Balaenoptera musculus and the fin whale B. physalus , reports of entangled individuals are rare, leading to the assumption that entanglements are not common. Studies of interaction with fisheries in other species often rely on the presence of scars from previous entanglements. Here, scar detection rates were first examined in humpback Megaptera novaeangliae , fin and blue whales using standard vessel-based photo-identification photographs collected between 2009 and 2016 in the Gulf of St. Lawrence, Canada. We then examined aerial images of fin whales collected with a drone in 2018 and 2019 and compared both methods. Entanglement rates were 6.5% for fin and 13.1% for blue whales using photo-identification images of individuals. Prominent scarring was observed around the tail and caudal peduncle, visible only when animals lifted those body sections above water when diving. For the small subset of pictures which captured the entire caudal peduncle, entanglement rates ranged between 60% for blue and 80% for fin whales. This result was similar to the 85% entanglement rate estimated in humpback whales. The assessment of aerial-based photography yielded an entanglement rate of 44.1 to 54.7% in fin whales. Scars were always around the peduncle, often the tail, rarely the dorsal fin and never around the pectoral fins, while the mouth cannot be examined from above. Thus, in species that do not regularly expose their tail or peduncle, aerial imagery is the preferred method to quantify entanglement rates by assessment of scars.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".