Identifying narwhal vocalizations to assess marine conservation areas in the changing Arctic
Bibliographic record
Abstract
Climate-change related increases in sea surface temperatures and associated declines in sea ice in the Arctic are driving phenological shifts in habitat use of several species of marine mammals, which are also facing higher noise levels from increasing human activities. While some protected areas have been established, their effectiveness for species of interest, such as narwhal, have not been assessed. Sound is important for communication and navigation in marine mammals, which makes passive acoustic monitoring an appropriate tool for studying these species, being particularly effective in remote and harsh Arctic environments. Recordings collected in the Disko Fan and Davis Strait Conservation Areas (southern Baffin Bay), known habitat for narwhals, are analyzed using a new method for identifying narwhal whistles in long-term passive acoustic datasets. We identify narwhal whistles by assuming that any whistles produced around the same time as narwhal echolocation clicks (manually identified on spectrograms) were produced by narwhal. These whistles are then used to train a new deep learning detector to process long-term passive acoustic datasets for narwhal presence. This is an important step toward understanding the impacts of climate change on the distribution of this species and the effectiveness of conservation areas.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".