Clonal evaluation for fusiform rust disease resistance: effects of pathogen virulence and disease escape
Bibliographic record
Abstract
We evaluated the precision of phenotypic classification for fusiform rust resistance of Pinus taeda L. in a clonally propagated population segregating for the pathotype-specific resistance gene Fr1. In all marker-defined Fr1/fr1 clones screened with low complexity or ambient inoculum, marker–trait cosegregation was complete with no exceptions. Uncommon exceptions (4 of 30) in which marker-defined Fr1/fr1 clones screened with high complexity inoculum were diseased were probably due to a low frequency of spores virulent to Fr1 resistance. Marker–trait cosegregation for fr1/fr1 clones was less reliable, as all ramets of a few clones (5 of 29, 3 of 25, and 4 of 16) remained disease-free with low complexity, high complexity, or ambient inoculum, respectively. We termed disease-free fr1/fr1 ramets “escapes”, since the genetics of the host–pathogen interaction predicted them to be diseased. For nonmarker-defined materials, we considered escapes to be disease-free ramets within clones that had at least one diseased ramet. Narrow-sense heritability estimates for escape rate were 29% and 23% for the low and high complexity inocula, respectively, suggesting that genetic variation in the host is an important component of this resistance mechanism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".