Lack of detectable genetic differentiation between den populations of the Prairie Rattlesnake (<i>Crotalus</i><i>viridis</i>) in a fragmented landscape
Bibliographic record
Abstract
Numerous studies have reported genetic fragmentation of species whose habitat has been modified by roads and other anthropogenic features, but it is still not clear how most species respond to roads and whether genetic effects can be detected over a limited number of generations. We used road-crossing models and population genetic analysis (based on microsatellite loci) to make inferences about functional connectivity between populations of the Prairie Rattlesnake (Crotalus viridis (Rafinesque, 1818)) on opposite sides of the Trans-Canada Highway near Medicine Hat (Alberta, Canada). The road-crossing model predicted a high probability of mortality while crossing the Trans-Canada Highway. However, model-based genetic clustering methods (STRUCTURE and BAPS) did not detect structure; a nonmodel-based clustering method (DAPC) found structure, but most groups consisted of individuals captured throughout the study area. Estimates of effective population size were immeasurably large and power to detect genic differentiation was diminished if the effective size exceeded 500; this reduction in power was intensified when the number of loci was reduced (from eight to five to account for null alleles). Our results corroborate accounts of long-distance migration by this species and indicate that genetic fragmentation may not be easily detectable over this spatial and temporal scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".