Not all maps are equal: Evaluating approaches for mapping vessel collision risk to large baleen whales
Bibliographic record
Abstract
Abstract A growing and increasingly globalised human population, requiring the movement of goods and commodities, is placing increasing demands on the maritime industry, resulting in a concurrent increase in global shipping activities. This has consequences for the marine environment, particularly for species vulnerable to the impacts of vessel traffic. For example, vessel collisions can result in sub‐lethal or fatal injuries for marine mammals, whilst vessel noise can cause acoustic masking that effectively reduces an animal's listening space, potentially impacting their communication, navigation and foraging capacity. While a number of parallel approaches to mapping collision risk to large whales have arisen, these methods vary in their focus, usually on either co‐occurrence, collision probability, or probability of mortality. However, little attention has been given to the implications of methodological choice and data selection on subsequent risk predictions. To assess differences between these approaches, we used a standardised input dataset comprised of telemetry‐point data from tagged bowhead whales, and satellite‐based Automated Identification System (AIS) data of spatial vessel movements covering the Davis‐Baffin Arctic Marine Area. We applied this data to eight different, previously published analyses for deriving areas of vessel risk. We found that the choice of risk mapping approach affected the location, and total area, identified as ‘high risk’, and that more computationally complex approaches did not necessarily equate to different predictions. There was considerable variation in the total area of ‘high risk’ predicted within each map (range = 20–42,246 km 2 ). Synthesis and Applications . The results underscore the importance of methodological transparency, informed data selection and careful interpretation when predicting collision risk. We provide practical recommendations for enhancing transparency when predicting risk, and discuss choice of approach suitable for different situations or management applications. It is critical that managers and policy makers are aware of the implications of applying different approaches when interpreting risk evaluation outputs.
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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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".