AIS underrepresents vessel traffic in Scotland's Marine Protected Areas
Bibliographic record
Abstract
Maritime traffic poses a variety of risks to both the marine environment and marine wildlife. To quantify and predict risk, accurate data on the distribution and densities of vessel traffic is required, yet currently there is no single data type that captures all vessel traffic. Most commonly, AIS (Automatic Identification System) vessel tracking data is used, despite awareness that AIS data does not fully capture all vessels present. Therefore, evaluations using only AIS likely underestimate the potential impacts. To estimate the scale of underestimation, vessel presence within six of Scotland's Marine Protected Areas (MPAs) were recorded during >1800 h of land-based and at-sea surveys, and compared with AIS data collected from a network of receivers deployed around Scotland. Non-AIS vessels were present within MPAs during 62 % of the surveyed period, with 64 % of vessels sighted not broadcasting AIS. AIS transmission rates varied between MPA, season and vessel type. Given that AIS data is the most commonly used data type for quantifying vessel activity and predicting associated impacts, consideration must be given to the volume of vessel traffic not represented within AIS datasets, particularly within MPAs. Underestimation of actual vessel traffic is likely leading to insufficient management or mitigation efforts within areas designated for protection. • AIS (Automatic Identification System) data often used to represent vessel traffic. • Surveys conducted across Scottish Marine Protected Areas. • AIS data did not represent vessel traffic within MPAs 62 % of the time. • 64 % of powered vessels sighted within MPAs were not broadcasting AIS. • Risk predictions using only AIS would underestimate the scale of potential impacts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".