The Temporal and Spatial Scale of Microevolution: Fine-scale Color Pattern Variation in the Lake Erie Watersnake
Why this work is in the frame
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Bibliographic record
Abstract
Question: What is the temporal and spatial scale of microevolution? \nHypotheses: The combined effects of natural selection and gene flow result in variation in \nheritable traits on fine spatial and geographic scales. \nOrganism: The Lake Erie watersnake, Nerodia sipedon insularum. \nField site: US and Canadian islands in western Lake Erie. \nMethods: We tested for variation in colour pattern frequency within islands, among islands, \nand over time using data from nearly annual censuses conducted since 1980, museum \nspecimens, and published sources. We compared FST for a presumptive major colour pattern \nlocus to FST for allozyme loci to determine whether spatial variation exceeded that expected by \nchance. We computed effective population size (Ne) based on temporal frequency changes in \npresumptive colour pattern alleles to determine whether temporal variation exceeded that \nexpected by chance (Ne significantly less than ∞). \nConclusions: Morph frequencies did not differ significantly within islands or between \nislands separated by short distances. Morph frequencies did sometimes differ significantly \namong distant islands and among sampling periods from 1980 to the present, but no more than \nexpected by chance. In contrast, a marked change in morph frequency occurred between \nhistoric (prior to 1961) and recent (1980–2003) samples. Possible mechanisms include changes \nin the strength of selection (due to changes in predator assemblages and visual environments) \nand rates of gene flow (due to changes in island watersnake population size).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it