Local Passive Acoustic Monitoring of Narwhal Presence in the Canadian Arctic: A Pilot Project
Bibliographic record
Abstract
ABSTRACT. Long-term community-based monitoring of narwhals (Monodon monoceros) is needed because narwhals are important to local Inuit and are facing changes in their environment. We examined the suitability of passive acoustic recording for monitoring narwhals, using data gathered in the Canadian Arctic from an autonomous acoustic recorder (Repulse Bay, 2006) and a hand-held digital recorder (Koluktoo Bay, 2006 – 08). We found a relationship between the number of narwhals observed passing a fixed point and the number of calls heard. In addition, we found that an automated call detector could isolate segments of recording containing narwhal vocalizations over long recording periods containing non-target sound, thus decreasing the time spent on the analysis. Collectively, these results suggest that combining passive acoustic sampling with an automated call detector offers a useful approach for local monitoring of the presence and relative abundance of narwhals. Key words: animal behaviour, automated detection, Baffin Island, marine mammal, narwhal, participatory monitoring RÉSUMÉ. La nécessité d’avoir un programme communautaire de surveillance à long terme des narvals (Monodon monoceros) s’avère évidente étant donné que les narvals revêtent de l’importance aux yeux des Inuits de la région et que leur environ-nement est en pleine évolution. Nous explorons la pertinence d’un programme de surveillance par acoustique passive pour les populations de narvals à partir de données récoltées dans l’Arctique canadien à l’aide d’une enregistreuse autonome (Repulse Bay, 2006) et d’une enregistreuse portable (Koluktoo Bay, 2006 – 2008). Grâce à des enregistrements accompagnés d’obser-vations sur le terrain, nous avons trouvé une corrélation entre le nombre de vocalisations entendues et le nombre de narvals
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".