Bibliographic record
Abstract
Birds are the most conspicuous, wide-ranging, and easily studied organisms in the marine environment. They can be both predators and scavengers, and they can be harmed by and can benefit from fishing activities. The effects of fishing on birds may be direct or indirect. Most direct effects involve killing by fishing gear, although on a lesser scale some fishing activities also disturb birds. Net fisheries and hook fisheries have both had serious negative effects at the population level. Currently, a major negative impact comes from the by-catch of albatrosses and petrels in long-lines in the North Pacific and in the Southern Ocean. High seas drift nets have had, prior to the banning of their use, a considerable impact on seabirds in the northern Pacific, as have gillnets in south-west Greenland, eastern Canada, and elsewhere. Indirect effects mostly work through the alteration in food supplies. Many activities increase the food supply by providing large quantities of discarded fish and wastes, particularly those from large, demersal species that are inaccessible to seabirds, from fishing vessels to scavengers. Also, fishing has changed the structure of marine communities. Fishing activities have led to depletion of some fish species fed upon by seabirds, but may also lead to an increase in small fish prey by reducing numbers of larger fish that may compete with birds. Both direct and indirect effects are likely to have operated at the global population level on some species. Proving the scale of fisheries effects can be difficult because of confounding and interacting combinations with other anthropogenic effects (pollution, hunting, disturbance) and oceanographic factors. Effects of aquaculture have not been included in the review.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".