Population subdivision of house mice (Mus musculus) in an agrarian landscape: consequences for control
Bibliographic record
Abstract
In Argentinean agroecosystems, house mice ( Mus musculus L., 1758) show a discontinuous distribution, with high abundances in farms but scarce abundance in crop fields. In our study area, the abundance of M. musculus could be affected by their movements among farms. We hypothesize that (1) M. musculus do not move among farms versus (2) M. musculus do move among farms. Furthermore, based on our second hypothesis, M. musculus move actively (hypothesis 2.1) or passively by human transport (hypothesis 2.2). Based on hypothesis 1, we predict that genetic subdivision will exist among farms and that genetic divergence will be independent of geographic distance. Based on hypothesis 2.1, genetic differentiation will be correlated with geographic distance. Based on hypothesis 2.2, genetic subdivision will be absent, or genetic differentiation will be related to human movements. We examined genetic variation among farms (n = 15) using five microsatellite loci and tracked the movements of 36 individuals from five farms with fluorescent powders. Populations of M. musculus showed genetic differentiation at both farm and shed scales. Genetic and geographic distances were significantly correlated. There was no evidence of passive movements of M. musculus. The movements of 36 M. musculus within farms, tracked with fluorescent powder, were short. According to these results, hypothesis 2.1 is favoured.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".