Allozyme genetic diversity among<i>Fagus grandifolia</i>trees resistant or susceptible to beech bark disease in natural populations
Bibliographic record
Abstract
American beech (Fagus grandifolia Ehrh.) trees resistant (R = 760) and susceptible (S = 681) to beech bark disease were located and mapped in nine natural stands in West Virginia (WV), Massachusetts (MA), Maine (ME), Nova Scotia (NS), and Prince Edward Island (PEI). Dormant bud tissue collected from each tree was examined by isozyme analysis to characterize the population genetic structure of R and S subpopulations mapped in each of four intensively sampled (R/S) stands, and five additional populations in which only R trees were sampled. Seventeen enzymes (with 9 polymorphic and 14 monomorphic loci) were analyzed to estimate variation across the spatial range of disease occurrence. All populations possessed significant levels of inter- and intra-population diversity; several parameters appeared to increase in magnitude from southwest to northeast across the range. In the R/S stands, percent polymorphic loci averaged 38%, average number of alleles/locus was 1.8 (3.0 for polymorphic loci), and mean expected heterozygosity was 0.165. Most genetic variation resided within populations (97%; GST= 0.03). Analyses of pooled R and S subpopulations demonstrated that observed heterozygosities were up to 26% higher in S trees, i.e., R tree populations exhibited consistent heterozygote deficits.
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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".