Microsatellite evaluation of haddock (<i>Melanogrammus aeglefinus</i>) stocks in the northwest Atlantic Ocean
Bibliographic record
Abstract
The goal of this study was to gain insight about the impact of intensive fishing on a single haddock (Melanogrammus aeglefinus) stock, and examine the genetic structuring of spatially discrete spawning aggregations in the northwest Atlantic. We analyzed genetic change at four microsatellite loci for Georges Bank haddock over a 40-year time span in which significant changes in demographics and abundances have occurred in the population. Allelic diversities have changed little, indicating that, although the commercial fishery has collapsed, stock sizes have remained large enough to insulate against major reductions in genetic variation due to drift. Results indicate significant genetic divergence among decadally separated samples. Potential causes for these differences include admixture from other spawning regions, fluctuations in the effective number of spawners contributing to a single spawning event, drift, or a combination of these. Examination of discrete spawning aggregations from Georges Bank, Browns Bank, the Scotian Shelf, and Nantucket Shoals indicated significant differences among stocks. Genetic distance based measures supported the clustering of Scotian Shelf, Browns Bank, and Georges Bank haddock to the exclusion of Nantucket Shoals haddock. Haddock spawning on Nantucket Shoals may be genetically discrete from other haddock populations in the northwest Atlantic.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".