Replicate divergence between and within sounds in a marine fish: the copper rockfish (<i><scp>S</scp>ebastes caurinus</i>)
Bibliographic record
Abstract
Understanding the factors that influence larval dispersal and connectivity among marine populations is critical to the conservation and sustainable management of marine resources. We assessed genetic subdivision among ten populations of copper rockfish (Sebastes caurinus) representing paired samples of outer coast and the heads of inlets in five replicate sounds on the west coast of Vancouver Island, British Columbia, using 17 microsatellite DNA loci. Overall, subdivision (F(ST)) was low (F(ST) = 0.031, P < 0.001), but consistently higher between paired coast and head of inlet sites (mean FST = 0.047, P < 0.001) compared to among the five coast sites (mean F(ST) = -0.001, P > 0.5) or among the five head of inlet sites (mean F(ST) = 0.026, P < 0.001). Heterozygosity, allelic richness and estimates of effective population size were also lower in head of inlet sites than in coast sites. Bayesian analysis identified two genetic groups across all samples, a single genetic group among only coast samples, two genetic groups among head of inlet samples and two genetic groups within each sound analysed separately. Head of inlet copper rockfish tended to be shorter with lower condition factors and grew more slowly than coast sites fish. Reduced physical connectivity and selection against immigrants in contrasting outer coast-head of inlet environments likely contribute to the evolution of population structure of copper rockfish. Based on genetic connectivity, coast sites appear to be better served by existing marine protected areas than are head of inlet sites.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".