Shifting gears: assessing collateral impacts of fishing methods in US waters
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
Problems with fisheries are usually associated with overfishing; in other words, with the deployment of “too many” fishing gears. However, overfishing is not the only problem. Collateral impacts of fishing methods on incidental take (bycatch) and on habitats are also cause for concern. Assessing collateral impacts, through integrating the knowledge of a wide range of fisheries stakeholders, is an important element of ecosystem management, especially when consensual results are obtained. This can be demonstrated using the “damage schedule approach” to elicit judgments from fishers, scientists, and managers on the severity of fishing gear impacts on marine ecosystems. The consistent ranking of fishing gears obtained from various respondents can serve as a basis for formulating fisheries policies that will minimize ecosystem impacts. Such policies include a shift to less damaging gears and establishing closed areas to limit collateral impacts.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it