Collaborative field research using drones for whale photo-identification studies in Cumberland Sound, Nunavut
Bibliographic record
Abstract
In conducting Arctic field research, hiring local field guides has long been a necessity for providing field teams with local knowledge and fundamental needs of boat operation and navigation, general field logistics/safety, and traditional ecological knowledge (TEK) of local animal distribution and natural history. As new threats to Arctic wildlife emerge and as field research methods evolve, including local Inuit as long-standing members of research teams has provided additional collaborative benefits through expanded local knowledge, greater efficiency of data collection, and longer temporal sampling which provides the opportunity to study uncommon events. We describe the collaboration between southern-based scientists and local Inuit from the community of Pangnirtung, Nunavut, to conduct field research on marine mammals in Cumberland Sound from 1997 to 2021. Through a keen interest in marine mammal field research, Inuit partners in Pangnirtung have become highly proficient in all aspects of sample and data collection and have received advanced technical training to allow for an expanded role in achieving research objectives. This expanded role includes running field research operations independently, as well as the extensive use of drones to capture photographs of whales for the purposes of photographic-identification and to record behavior. Collaboration with local Inuit also provides benefits through employment opportunities, development of technical skills, and opportunities to actively participate in research that aims to conserve culturally important local wildlife 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".