Population differentiation among three species of white oak in northeastern Illinois
Bibliographic record
Abstract
We used microsatellite DNA analysis to examine population differentiation among three species of white oak, Quercus alba L., Quercus bicolor Willd., and Quercus macrocarpa Michx., occurring in both pure and mixed stands in northeastern Illinois. Using individual-based Bayesian clustering or principal components analyses, no strong genetic groupings of individuals were detected. This suggests that the three species do not represent distinct and differentiated genetic entities. Nevertheless, traditional approaches where individuals are pre-assigned to species and populations, including F statistics, allele frequency analysis, and Nei's genetic distance, revealed low, but significant genetic differentiation. Pairwise F statistics showed that some intraspecific comparisons were as genetically differentiated as interspecific comparisons, with the two populations of Q. alba exhibiting the highest level of genetic differentiation (θ = 0.1156). A neighbor-joining tree also showed that the two populations of Q. alba are distinct from one another and from the two other species, while Q. bicolor and Q. macrocarpa were genetically more similar. Pure stands of Q. macrocarpa did not show a higher degree of genetic differentiation than mixed stands.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".