Sensitivity and Vulnerability in Marine Environments: an Approach to Identifying Vulnerable Marine Areas
Bibliographic record
Abstract
Abstract: Marine environments have suffered from a lack of quantitative methods for delineating areas that are sensitive or vulnerable to particular stresses, natural and anthropogenic. We define sensitivity as the degree to which marine features respond to stresses, which are deviations of environmental conditions beyond the expected range. Vulnerability can then be defined as the probability that a feature will be exposed to a stress to which it is sensitive. Using these definitions, we provide a quantitative methodology for identifying vulnerable marine areas based on valued ecological features, defined as biological or physical features, processes, or structures deemed by humans to have environmental, social, cultural, or economic significance. The vulnerability of the valued ecological features is a function of their sensitivity to particular stresses and their vulnerability to those stresses. We used the methodology to demonstrate how vulnerable marine areas for two groups of endangered whale species (inshore and offshore) could be identified with a predictive habitat model and acoustic stress surfaces. Acoustic stress surfaces were produced for ferry traffic, commercial shipping traffic, potential offshore oil production, and small‐boat traffic. The vulnerabilities of the two whale groups to the four stressors considered in this example were relatively similar; however, inshore species were more sensitive to on‐shelf, coastal activities such as offshore hydrocarbon production, ferry traffic, and small‐boat traffic. Our approach demonstrates how valued features can be associated with stresses and the likelihood of encountering these stresses (vulnerability) in order to identify geographic areas for management and conservation purposes. The method can be applied to any combination of valued ecological features and stressors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".