Use of satellite imagery to estimate distribution and abundance of Cumberland Sound beluga whales reveals frequent use of a glacial river estuary
Bibliographic record
Abstract
Limiting disturbance in critical habitats is an important part of ensuring the well-being and sustainability of populations at risk, such as Cumberland Sound beluga whales ( Delphinapterus leucas ). Using non-disruptive Very High Resolution (VHR) satellite imagery, an emerging tool in cetacean monitoring, we aimed to estimate summer abundance and identify critical habitat for Cumberland Sound beluga whales. Specifically we looked in fiords that comprise their summer distribution, such as Clearwater Fiord where there is a large estuary, an important habitat to many beluga populations. Satellite images of the area were collected in 2020 and 2021, at 30 cm resolution, and in 2022 at 50 cm resolution. We evaluated beluga whale distribution using Kernel density, and identified critical habitats as areas consistently part of the beluga whale core distribution across years. Clearwater Fiord abundance estimates were corrected for whales that were too deep to be identified in the images. The estimates were significantly lower in 2021 (197 whales, 95%CI: 180-216) and 2022 (194 whales, 95%CI: 172-218) compared to 2020 (393 whales, 95%CI: 366-422). Other fiords were only imaged in 2021 and 2022, resulting in average corrected abundance estimates for all fiords of 462 (95% CI: 425-502) and 252 (95%CI: 226-280) beluga whales, respectively. Downsampling of 30 cm images to 50 cm resulted in up to 45% fewer whales detected. The only critical habitat identified within the summer distribution was in Clearwater Fiord, in or near the estuary freshwater plume and in a bay to the west of the plume. The identified critical habitats should be areas of consideration in the continued discussion on the protection and sustainability of the Cumberland Sound beluga whale population.
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How this classification was reachedexpand
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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".