Taking stock: defining populations of American shad (Alosa sapidissima) in Canada using neutral genetic markers
Bibliographic record
Abstract
Knowledge of the scale of population structure is a prerequisite for designating conservation units. American shad ( Alosa sapidissima ) are of increasing conservation concern, but the scale of population structure within the Canadian portion of the species range is unknown. Using 13 microsatellite loci, we examined the partitioning of genetic variation within four and among 12 Canadian drainages. We detected significant (p < 0.05) and temporally stable genetic differentiation among all drainages, supporting the hypothesis that rivers support genetically distinct populations. However, Bayesian methods identified seven clusters and provided evidence for shad metapopulation structure. We observed a significant (p < 0.01) pattern of isolation by distance (IBD) among all drainages. A strong linear IBD (r = 0.98) was observed among rivers that were outside the Bay of Fundy (BoF). A hypothesized counterclockwise migration route explained a greater proportion of genetic variation (r = 0.87) among BoF rivers than direct route based distances (r = 0.14). Although IBD patterns did not differ regionally (analysis of covariance; p > 0.05), the degree of differentiation among BoF rivers was greater than that among non-BoF rivers, regardless of the geographic scale of comparison. Our results suggest that fisheries managers need to be concerned with the loss of shad genetic diversity on both river and regional scales.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".