Early genetic testing of coastal Douglas-fir for Swiss needle cast tolerance
Bibliographic record
Abstract
The possibility of early testing coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) for Swiss needle cast (SNC; caused by Phaeocryptopus gaeumannii (Rohde) Petrak) tolerance was investigated using 55 Douglas-fir families from western Oregon. Seedlings were inoculated with P. gaeumannii naturally in the field and were visually scored for a variety of SNC symptom traits (i.e., needle and foliage color, and retention) at the seedling stage (age 2) and in "mature" (ages 10 and 12) trees at two test sites for both the seedling and mature ages. Seedlings were also assessed in the laboratory for SNC symptom traits, for proportion of needle stomata occluded with pseudothecia (PSOP), and for amount of P. gaeumannii DNA in needles. Although families differed significantly at both ages for all SNC symptom traits and for PSOP, they did not differ for amount of fungal DNA. Thus, genetic variation in SNC symptoms appears to be primarily due to differences in tolerance to the disease rather than to resistance to infection per se. Estimated individual-tree heritabilities for SNC symptom traits were low to moderate (mean hi2 = 0.19, range 0.060.37) at both ages, and within each age-class these traits were moderately to strongly genetically correlated (mean rA = 0.69, range 0.420.95). Type B genetic correlations between SNC symptom traits in seedlings and mature trees ranged from 0 to 0.83 and were weakest for traits measured in the laboratory. Genetic gain estimates indicated that family selection for SNC tolerance (i.e., greener needles or greater foliage retention) at the seedling stage can be very effective in increasing tolerance in older trees.
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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 | 0.000 | 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".