Spatial Impact of Recreational-Grade Echosounders and the Implications for Killer Whales
Bibliographic record
Abstract
The spatial extent of a dual-frequency echosounder and its potential impact on cetacean species were examined. Sound emissions of output frequencies of 83 kHz and 200 kHz were tested at a maximum distance of 400 m. This is the minimum vessel approach distance for killer whales (Orcinus orca) in southern British Columbia, which was introduced as a measure for limiting disturbance of southern resident killer whales (SRKWs). The experiment was conducted in shallow (34 m) and deep (220–235 m) water. Recordings were made at depths of 5 and 30 m for both locations, as well as at 100 and 200 m in the deeper water to examine the effect of the echosounder through the water column and at SRKW diving depths. The recordings showed that 200 kHz echosounder emissions were contained within a 250 m radius of the source, with most of the acoustic energy focused within 100 m in shallow waters. For the 83 kHz signal and for deeper water testing of the 200 kHz signal, the echosounder transmissions exceeded the 400 m threshold, intimating that whales could experience noise additions of 30 dB or more above the ambient level at the minimum vessel approach distance. Evaluating the noise additions to the ambient level from the echosounder in frequencies used by SRKWs for echolocation (15–100 kHz) further showed the potential impact on whales in close proximity to vessels (≤400 m) when using echosounders or fish-finders.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".