Using satellite imagery to estimate abundance of Cumberland Sound beluga whales (Delphinapterus leucas) in 2021
Bibliographic record
Abstract
The Cumberland Sound beluga whale (Delphinapterus leucas) population inhabits Cumberland Sound on the southeast side of Baffin Island, Nunavut. The population is listed as threatened under the Species at Risk Act. The last abundance estimate from an aerial survey was estimated at 1,381 (95% CI: 1,270-1,502) beluga whales in 2017 for an area covering 12,485 km2. Since then, satellite imagery has been used as a remotely based non-invasive method to monitor these whales. Very High Resolution (VHR) satellite imagery covering 9,690 km² of water was collected from Cumberland Sound from August 30 to September 7, 2021, during the ice free season. Readers with previous imagery analysis experience analyzed the images and identified 704 certain detections. Abundance estimates were corrected for availability bias for whales that were too deep to be detected in the imagery (>2m). We present a total estimate of 1,690 (CV = 0.16; 95% CI: 1,241-2,301) beluga whales in Cumberland Sound (22,663 km2). This estimate covers a larger area and estimates a higher abundance than the 2017 aerial survey. Regular population abundance assessments are essential for understanding population dynamics and trends and we have shown here that satellite imagery is a comparable method to aerial surveys for estimating abundance.
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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 | 0.002 | 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) | 0.000 | 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 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".