Identifying Beluga Distribution in the Tarium Niryutait Marine Protected Area using Passive Acoustic Monitoring
Bibliographic record
Abstract
Arctic estuaries are important summer habitats for beluga whales (Delphinapterus leucus) and many populations form seasonal aggregations at these locations. This study presents the first comprehensive long-term analysis of beluga distribution within the Mackenzie Estuary since the 1970s and 1980s. Leveraging eight years of passive acoustic monitoring data, we assess the consistency of beluga habitat use over time and space by comparing vocalization rates at select monitoring locations annually and providing a benchmark upon which to monitor ecological change in the Tarium Niryutait Marine Protected Area. Findings reveal temporal consistency in beluga distribution and demonstrate site fidelity in alignment with known habitat hotspots; however, results also highlight a degree of inter-annual variability in beluga habitat use, indicating that belugas may alter their distribution in response to environmental and anthropogenic factors. Additionally, we develop a suite of simple univariate metrics to define the timing of belugas movements to and from the estuary. Our data support previous aerial survey findings and Inuvialuit Knowledge that beluga entry into the estuary is closely tied to the timing of ice breakup. Characteristics of the annual beluga aggregation should be considered in the relation to ice breakup date when interpreting indicators of change in habitat use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 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".