Satellite image survey of beluga whales in the southern Kara Sea
Bibliographic record
Abstract
Abstract The use of satellite imagery to find, count and monitor whales in remote and hard to access areas has shown some promise, but few satellite studies have, as yet, provided substantial conservation outcomes. Recent studies have shown the ability of very high‐resolution satellites to detect and count previously surveyed populations of belugas and narwhals in Canada. Here we describe the detection of a large aggregation of a poorly surveyed population of belugas in the southern Kara Sea, Russia, in a region where Soviet whaling is known to have had a heavy toll on belugas. We counted over 1,100 surface belugas using very high‐resolution satellite imagery. As only an unknown portion of the belugas can be seen on the surface, accurately converting the surface count to an abundance estimate will need further study, but using the analog of aerial surveys we estimate that this aggregation is between ~1,150–2,870 individuals. Although the species is not currently considered endangered, concern over belugas future population trends is increasing, as the species is reliant on Arctic sea ice, which is rapidly declining due to climate change. This study shows the utility of satellite imagery to discover and monitor new and little‐known cetacean populations.
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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 | 0.001 |
| 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".