How we can all stop killing whales: a proposal to avoid whale entanglement in fishing gear
Bibliographic record
Abstract
Abstract Whales are federally protected by the Marine Mammal Protection Act; endangered species, such as the North Atlantic right whale, receive additional protection under the Endangered Species Act. However, their regulations have failed to satisfy conservation and animal welfare concerns. From 1990 to 2011 the North Atlantic right whale (Eubalaena glacialis, NARW) population grew at a mean of 2.8% annually. However, population trends reversed since 2011; the species is in decline, with only ∼100 reproductively active females remaining. This failure is driven by vessel collisions and increasingly fatal and serious entanglement in fixed fishing gear, whose rope strength has increased substantially. Chronic entanglement, drag, and associated morbidity have been linked to poor fecundity. Genuine solutions involve designating areas to be avoided and speed restrictions for ships and removing fishing trap ropes from the water column. A trap fishing closure for NARW habitat in the Cape Cod Bay (U.S.) area has been in place seasonally since 2015. 2017 mortalities in Eastern Canada elicited substantive management changes whereby the 2018 presence of NARW in active trap fishing areas resulted in an effective closure. To avoid these costly closures, the traditional trap fishery model of rope end lines attached to surface marker buoys has to be modified so that traps are marked virtually, and retrieved with gear that does not remain in the water column except during trap retrieval. Consumer demand for genuinely whale-safe products will augment and encourage the necessary regulatory changes so that trap fisheries conserve target and nontarget species.
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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.029 | 0.017 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".