Policy enabling North Atlantic right whale reproductive health could save the species
Bibliographic record
Abstract
Abstract North Atlantic right whales (Eubalaena glacialis) risk extinction unless conservation measures not only reduce mortality but also enhance reproduction. Vessel collisions injure and kill by spinning propeller cuts or being hit with a blunt structure resulting in bone and soft tissue damage. Entanglement trauma includes sublethal injuries that can reduce their ability to reproduce, while lethal events include drowning, deep constricting wounds, and emaciation leading to death over months or years. Current regulations attempt to reduce mortality from vessel strikes and fishing gear entanglement off the eastern shores of the United States and Canada. However, sub-lethal stressors, especially entanglement, have exacerbated impacts from climate-driven food supply changes, resulting in a serious reduction in growth of individuals and calving rates. If consumers demand that their ship-borne goods and bottom-caught seafood be procured without serious welfare and health concerns for the whales, recovery is possible. We need the will to widely employ the tools of vessel speed restrictions and acoustic retrieval of bottom traps and nets without a persistent vertical line in the water column to reduce sub-lethal as well as lethal trauma. Thus, consumers should pressure legislators, endangered species managers, and suppliers for far broader protections than currently exist.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".