Bowhead Whale Drone Data Collection - Cumberland Sound - Nunavut
Bibliographic record
Abstract
We summarize the drone-based data collected for Bowhead Whales (Balaena mysticetus) in Cumberland Sound, Nunavut, in collaboration with researchers from Dalhousie University and the community of Pangnirtung. The datasets highlighted in this record focus on drone observations, which represent the primary contribution of the Hakai Institute, with additional project components summarized for context. Data was collected under DFO animal care permit 2023-2024 / marine mammal license for whale research, all methods were performed in accordance with the relevant guidelines and regulations. Our objectives are to: 1) Develop a more complete understanding of the ecosystem conditions that support current populations of bowhead whales in the eastern Canadian Arctic 2) Evaluate the health and condition of different age-sex groups of whales based on morphometrics 3) Predict future impacts on the nutritional regime of future populations of EC-WG bowhead whales. Drone operations were conducted to capture still images and altimeter data for measuring body size and health. We also used drone video to monitor behaviors, assist with fecal sample collection, and ensure safe boat operations around the whales. We employed two oceanographic sampling approaches: opportunistic sampling in the path of feeding whales, and systematic surveys along predetermined track lines to cover a broader area, both in the presence and absence of whales. We used net collections for DNA barcoding to differentiate between species of Calanus. To confirm that the zooplankton species we sampled reflected the Bowhead Whale diet, we opportunistically collected scat from live whales and stomach contents from harvested Bowheads, using DNA metabarcoding to identify species composition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".