Propagation of narrow-band-high-frequency clicks: Measured and modeled transmission loss of porpoise-like clicks in porpoise habitats
Bibliographic record
Abstract
Estimating the range at which harbor porpoises can detect prey items and environmental objects is integral to understanding their biosonar. Understanding the ranges at which they can use echolocation to detect and avoid obstacles is particularly important for strategies to reduce bycatch. Transmission loss (TL) during acoustic propagation is an important determinant of those detection ranges, and it also influences animal detection functions used in passive acoustic monitoring. However, common assumptions regarding TL have rarely been tested. Here, TL of synthetic porpoise clicks was measured in porpoise habitats in Canada and Denmark, and field data were compared with spherical spreading law and ray-trace (Bellhop) model predictions. Both models matched mean observations quite well in most cases, indicating that a spherical spreading law can usually provide an accurate first-order estimate of TL for porpoise sounds in porpoise habitat. However, TL varied significantly (+/-10 dB) between sites and over time in response to variability in seafloor characteristics, sound-speed profiles, and other short-timescale environmental fluctuations. Such variability should be taken into account in estimates of the ranges at which porpoises can communicate acoustically, detect echolocation targets, and be detected via passive acoustic monitoring.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".