Admixture increases diversity in managed honey bees: Reply to De la Rúa <i>et al</i>. (2013)
Bibliographic record
Abstract
De la Rúa et al. (2013) express some concerns about the conclusions of our recent study showing that management increases genetic diversity of honey bees (Apis mellifera) by promoting admixture (Harpur et al. 2012). We provide a brief review of the literature on the population genetics of A. mellifera and show that we utilized appropriate sampling methods to estimate genetic diversity in the focal populations. Our finding of higher genetic diversity in two managed A. mellifera populations on two different continents is expected to be the norm given the large number of studies documenting admixture in honey bees. Our study focused on elucidating how management affects genetic diversity in honey bees, not on how to best manage bee colonies. We do not endorse the intentional admixture of honey bee populations, and we agree with De la Rúa et al. (2013) that native honey bee subspecies should be conserved.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.031 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".