Population genomics of coastal Pacific Hake
Bibliographic record
Abstract
Abstract Objective Understanding the genetic structure of harvested fishery species is crucial for accurate stock assessments and effective management strategies. There have been several rangewide population genetic analyses of Pacific Hake Merluccius productus; however, a thorough focus on the heavily harvested coastal stock off the west coast of North America is missing. Recent observations of spatial–temporal variability in life history and migratory patterns of the coastal population have brought into question whether this variation may be related to genetic differentiation. Methods Here, we used restriction site-associated DNA sequencing markers to thoroughly assess the potential for spatial–temporal genetic differentiation in the coastal stock of Pacific Hake. We sampled during different seasons from British Columbia down to the U.S.–Mexico border over multiple years on what traditionally have been thought to constitute spawning and feeding grounds, resulting in the most comprehensive assessment of coastal Pacific Hake population structure to date. Result Generally, our results suggest very weak to no structure among coastal spatial–temporal sites and corroborate previous findings of strong differentiation between coastal and Salish Sea populations. The lack of structure among coastal sites is likely due to significant amounts of gene flow in this highly migratory population. Conclusion These findings align with the ongoing management strategy for coastal Pacific Hake, which is based on an annual stock assessment that considers the coastal stock homogenous and distinct from the Salish Sea population. The understanding that management units indeed match genetic populations provides managers with additional confidence in existing management strategy.
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 | 0.000 |
| 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".