Up in the air: drone images reveal underestimation of entanglement rates in large rorqual whales
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.003 | 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