A beluga whale distribution dataset for the Canadian sub-Arctic derived from 2021–2022 WorldView imagery
Bibliographic record
Abstract
This dataset is derived from WorldView-2/3 satellite imagery acquired in 2021–2022 over key summer habitats of beluga whales in the Canadian sub-Arctic , including northwestern and southeastern Cumberland Sound and the Churchill River estuary. It contains three annotated classes: certain beluga, uncertain beluga, and harp seal. The dataset was developed through a workflow combining manual annotation, deep learning inference, and manual verification. Systematic manual interpretation was first conducted in northwestern Cumberland Sound and the Churchill River to build training samples for object detection. A trained YOLOv8 model was then applied to southeastern Cumberland Sound for large-scale inference, followed by manual review. The final dataset includes geolocations of 721 certain belugas, 477 uncertain belugas, and 551 harp seals. This resource supports studies on the distribution and abundance estimation of beluga whales and harp seals, as well as training and evaluation of object detection algorithms, and provides a useful reference for polar ecological conservation and sustainable tourism planning.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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".