Locations of features of interest from a multi-season (2018-2022) baleen whale-focused survey of Wilhelmina Bay, Western Antarctic Peninsula, using WorldView-03 satellite imagery
Bibliographic record
Abstract
The application of Very-High-Resolution satellite imagery for the purpose of studying wildlife, particularly in remote regions, has gained significant traction in recent years. With this there has been an exponential increase in the volume of data, which has fostered a shift towards the use of automated systems to increase processing efficiency. However, these systems require manually annotated data on which to be trained, which is lacking. This dataset describes a total of 819 annotated and classified whale Features of Interest (FOIs) from a multi-season survey of Wilhelmina Bay on the Western Antarctic Peninsula (WAP). These data are comprised of FOIs that have been annotated and classified based on existing protocols by seven individual observers who scanned ~1,900 km2 of WorldView-03 imagery acquired between 2018/2019 and 2021/2022. This work was supported by an Innovation Voucher from the British Antarctic Survey and grants from the World Wildlife Fund (GB107301) and NC-International NERC (NE/T012439/1).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".