Evidence-informed conservation policies: Mitigating vessel noise within gray whale (Eschrichtius robustus) foraging habitat in British Columbia, Canada [graduate project].
Bibliographic record
Abstract
Anthropogenic noise is increasing within our oceans from growing human use. This rise in the ambient soundscape of the marine environment is increasing pressure on the life processes and health of marine animals. Cetaceans rely on the use sound for their life processes, and are thereby particularly susceptible to anthropogenic noise, like that from boats and other vessels. Whale watching vessels are directly exposing whales to their noise output. The current literature postulates that baleen whales are less susceptible to smaller vessels, like whale watching boats, as smaller boats emit high frequency sound, presumed out of the range of baleen whale low frequency communication. This interaction is analyzed within the foraging habitat of the eastern Pacific gray whales (Eschrichtius robustus) in Clayoquot Sound, British Columbia using passive acoustic monitoring. Noise disturbance from whale watching vessels is investigated using acoustics to analyze the contribution of vessel noise to the background sound levels of gray whale foraging habitat, and the differences in gray whale vocalizations in the presence of vessel noise. Evidence of acoustic disturbance is coupled with an analysis of the current policy regime and characterization of the Tofino whale watching fleet whale encounters to recommend future management and policy adoption to minimize cumulative impacts of vessel noise on gray whales. The enablers and barriers to evidence use within policy and management are identified to ease amendments to the current strategies for effective whale conservation in BC. This evidence-use approach supports strengthening acoustic protection of cetaceans, which assists in safeguarding the local tourism activities of whale watching.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".