A Monte Carlo approach to modelling detection ranges for killer whales in inshore waters of British Columbia, Canada
Bibliographic record
Abstract
Passive underwater listening stations are often used to monitor the presence, distribution and movements of marine mammals. This requires an understanding of the distances at which marine mammal sounds can be detected at a location and at different times given varying ambient noise conditions. Here, we describe a Monte Carlo approach for determining call detection probabilities as a function of distance for networked underwater listening stations deployed by Fisheries and Oceans Canada in the Salish Sea to track endangered Southern Resident Killer Whales. We used ambient sound levels measured in situ, modelled propagation losses determined by two acoustic models, and applied Monte Carlo simulations to capture the variability in call source level and animal depth. Given that only some parts of the call frequency spectrum may be responsible for the maximum detection range, the analysis was carried out independently for consecutive 300 Hz frequency bands. Median detection range estimates ranged from 700 m at the noisiest time and location to 8 km at the quietest. A sensitivity analysis revealed that the frequency distribution of source levels used for the analysis was a major factor affecting detection range results, while differences in propagation losses between summer and winter were less important.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".