Spatial and temporal genetic stock composition of river herring bycatch in southern New England Atlantic herring and mackerel fisheries
Bibliographic record
Abstract
Anadromous river herring (alewife and blueback herring) persist at historically low abundances and are caught as bycatch in commercial fisheries, potentially preventing recovery despite conservation efforts. We used newly established single-nucleotide polymorphism genetic baselines for alewife and blueback herring to define fine-scale reporting groups for each species. We then determined the occurrence of fish from these reporting groups in bycatch samples from a Northwest Atlantic fishery over four years. Within sampled bycatch events, the highest proportions of alewife were from the Block Island (34%) and Long Island Sound (22%) reporting groups, while for blueback herring the highest proportions were from the Mid-Atlantic (47%) and Northern New England (24%) reporting groups. We then quantified stock-specific mortality in a focal geographic area (∼3500 km2, including Block Island Sound) of high bycatch incidence and sampling effort, where the most accurate estimates of mortality could be made. During this period, we estimate that bycatch took about 4.6 million alewife and 1.2 million blueback herring, highlighting the need to reduce bycatch mortality for the most depleted river herring stocks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".