Marine monitors: Land-based citizen science observations show AIS data underrepresents coastal vessel traffic co-occurring with marine mammals
Bibliographic record
Abstract
Marine mammals are vulnerable to a variety of impacts from vessels, including underwater noise disturbance and injury from collision. To quantify this risk, AIS (Automatic Identification System) vessel tracking data is often used as a proxy of vessel presence. However, many vessels do not appear within AIS datasets, meaning evaluating impacts using AIS alone will likely underestimate the potential for effects to occur. To understand the scale of underestimation and the types of vessels co-occurring with marine mammals, 3-yrs of land-based surveys recorded all vessels (sighted within ∼10 km of shore) that were observed concurrently with whales, dolphins and seals (sighted within ∼3 km of shore). Surveys were conducted from multiple sites within five Scottish Marine Regions. Observations of responses to vessels were also recorded opportunistically. AIS data accurately reflected coastal vessel traffic co-occurring with marine mammals during 30 % of the surveyed period. 59 % of vessels seen were not broadcasting AIS, with seasonal and spatial variation in AIS transmission rates, with lowest AIS transmission rates in summer (when 38 % of co-occurring vessels were broadcasting AIS). Non-AIS vessels were more frequently observed travelling at speeds that may pose an elevated risk to marine mammals, and were also more frequently recorded to elicit a response. 45 % of responses to vessels involved non-AIS powered vessels, and 33 % were in relation to human-powered vessels (e.g. kayaks). The results show that the majority of vessels that co-occur with marine mammals are non-AIS, and as such AIS data alone is insufficient to represent vessel-related impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".