Variation in glider-detected North Atlantic right, blue, and fin whale calls in proximity to high-traffic shipping lanes
Bibliographic record
Abstract
Passive acoustic monitoring has become an integral tool for determining the presence, distribution, and behavior of vocally active cetacean species. Acoustically equipped underwater gliders are becoming a routine monitoring platform, because they can cover large spatial scales during a single deployment and have the capability to relay data to shore in near real-time. Yet, more research is needed to determine what information can be derived from glider-recorded cetacean detections. Here, a Slocum glider that monitored continuously for low frequency (<1 kHz) baleen whale vocalizations was deployed across the Honguedo Strait and the associated traffic separation scheme in the Gulf of St. Lawrence, Canada, during September and October 2019. We conducted a manual analysis of the archived audio to examine spatial and temporal variation in acoustic detection rates of North Atlantic right whales (NARWs), blue whales, and fin whales. Call detections of blue and fin whales demonstrated that both species were acoustically active throughout the deployment. Environmental association models suggested their preferential use of foraging areas along the southern slopes of the Laurentian Channel. Results also indicate that elevated background noise levels in the shipping lanes from vessel traffic only minimally influenced the likelihood of detecting blue whale acoustic presence, while they did not affect fin whale detectability. NARWs were definitively detected on less than 20% of deployment days, so only qualitative assessments of their presence were described. Nevertheless, detections of all 3 species highlight that their movements throughout this seasonally important region overlap with a high volume of vessel traffic, increasing their risk of ship strike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".