Homogenization of Populations in the Wildflower, Texas Bluebonnet (Lupinus texensis)
Bibliographic record
Abstract
Wildflower seeds are routinely spread along highways and thoroughfares throughout North America as part of federal beautification policy, but the genetic effect of the introduction of these cultivated populations on wild populations of the same species is unknown. Interbreeding may occur between these seeded and wild populations, resulting in several possible outcomes. Here we sample 187 individuals in 12 matched pairs of neighboring wild and seeded populations of the Texas bluebonnet (Lupinus texensis), a species popular in commercially available wildflower seed mixes used by both the Texas Department of Transportation and the public. We use genotyping by sequencing to identify 11741 genome-wide single nucleotide polymorphisms, as well as a smaller number of SNPs from the chloroplast genome, to analyze population structure and genetic diversity within and between the populations. We find a striking lack of population structure both between wild and seeded populations and amongst wild populations. STRUCTURE analyses indicate that all populations are apparently panmictic. This pattern may be explained by extensive swamping of wild populations by seeded germplasm and increased dispersal of semi-domesticated seed across this species' core native range by humans. We discuss the possible negative and positive ramifications of homogenization on the evolutionary future of this popular wildflower species.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".