Collaborative field research using drones for whale photo-identification studies in Cumberland Sound, Nunavut
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 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 it