Assessment of beech scale resistance in full- and half-sibling American beech families
Bibliographic record
Abstract
A beech bark disease infested American beech tree ( Fagus grandifolia Ehrh.) and two uninfested trees were selected in a mature natural stand in Michigan, USA, and mated to form two full-sib families for evaluating the inheritance of resistance to beech scale ( Cryptococcus fagisuga Lind.), the insect element of beech bark disease. Four half-sib families from both infested and uninfested trees were also evaluated for resistance. Using an artificial infestation technique, adult and egg count data were collected over 2 years and analyzed with generalized linear mixed methods to account for nonnormal distributions of the response variables. A significant effect for family was found for each variable. Family least squares means were computed as a measure of resistance and repeatabilities were calculated to provide an upper limit estimate of broad-sense heritability. The two families that ranked highest for resistance were the full-sib family from two uninfested parents and the half-sib family from a stand where all diseased trees had been removed. Together, the results suggest that selection and breeding may be an effective means to improve populations for artificial regeneration, and silvicultural treatments may provide an effective management option for mitigating beech bark disease through managing the genetic composition of natural regeneration.
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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 | 0.000 | 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) | 0.001 | 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".