Proximity analysis between icebreakers and narwhals in Tasiujaq, Nunavut, Canada
Bibliographic record
Abstract
Abstract Icebreaker vessels in the Canadian Arctic are used to monitor and maintain open shipping lanes during summer and fall. Icebreaker operating routes often overlap with important habitats of endemic Arctic marine mammals, leading to potential interactions and disturbance. In this study, we examined the frequency, proximity, and duration of encounter events between narwhals (Monodon monoceros) and icebreaker vessels in Tasiujaq (Eclipse Sound), Nunavut, an important summering area for this species. We combined tracking data from 25 narwhals equipped with satellite telemetry devices in Tasiujaq between 2016 and 2018 with automatic identification system (AIS) data for the same period from 7 icebreaker vessels. We defined an encounter event between a narwhal and an icebreaker as the total consecutive occurrences of an individual within 50 km of a vessel within 1 hour. For all years combined, 17 out of 25 narwhals had at least 1 encounter with an icebreaker and all 7 icebreakers were involved in at least 1 encounter. The closest distance (closest point of approach) recorded was <1 km and the longest cumulative duration of encounter events for 1 narwhal with 1 icebreaker was 121.6 hours (n = 27 encounter events over 17 days). Proximity and continuous exposure of narwhals to high levels of noise are a key concern when considering short‐ and long‐term effects on behavior and fitness of this species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".