Reply to the comment by Beacham and Withler on “Gene flow increases temporal stability of Chinook salmon (Oncorhynchus tshawytscha) populations in the Upper Fraser River, British Columbia, Canada”Appears in Can. J. Fish. Aquat. Sci.<b>66</b>: 167–176.
Bibliographic record
Abstract
Beacham and Withler (2010. Can. J. Fish. Aquat. Sci. 67: 202–205) raise concerns about the experimental design and interpretation of data in the analysis of temporal genetic variation of Chinook salmon ( Oncorhynchus tshawytscha ) from the Upper Fraser River, Canada (Walter et al. 2009. Can. J. Fish. Aquat. Sci. 66: 167–176). They note that for the sampled populations, spatial genetic variance should far exceed temporal variance components based on previously published work and suggest that limited sample sizes biased our results by confounding sampling error with temporal variation. Here, we perform a rarefaction analysis by randomly removing up to 50% of the individuals from sample sites, yet the pattern of temporal versus spatial variation is similar to that reported in our original paper. We reiterate that caution should be applied to the interpretation of migration rates estimated from assignment tests, yet the absolute magnitude of our migration estimates was not central to the goals of the original paper. Although Beacham and Withler raise important points on the validation of genetic stock identification analyses, our analyses of temporal variation in genetic population structure in the Upper Fraser River population likely differ due to demographic differences between the timing of sampling of their earlier work versus our analyses.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.027 | 0.037 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".