A decision framework to identify populations that are most vulnerable to the population level effects of disturbance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We present a decision framework to identify when detailed population‐level assessments are required to understand the potential impacts of a disturbance‐inducing activity on a marine mammal population and discuss how the framework can be applied to other taxa. Species at high risk of population‐level effects can be identified using information on the number of individuals that are likely to be disturbed by the activity, total population size, the probability of repeated disturbance, the species' reproductive strategy, and the life stages (e.g., feeding, pregnant, and lactating) of the individuals most likely to be exposed. This hierarchical approach provides those responsible for conducting impact assessments with a time‐efficient, cost‐effective and reproducible workflow that allows them to prioritize their efforts and assign funds to those species with the most pressing conservation needs. A fully worked case study using marine mammals in the vicinity of a naval training activity is supplied.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it