Genetic variation in decay resistance and its correlation to wood density and growth in white spruce
Bibliographic record
Abstract
This study investigated the genetic variation of white spruce (Picea glauca (Moench) Voss) in decay resistance and its correlation with wood density and growth. Three fungi were examined, a brown-rot fungus (Gloeophyllum trabeum), a white-rot fungus (Trametes versicolor), and a standing-tree-decay fungus (Fomitopsis pinicola). The decay resistance was inversely related to the growth rate of the fungi on heartwood blocks. A total of 270 trees of 35 families were harvested from 36-year-old provenanceprogeny trials at two sites through a thinning operation. The narrow-sense heritabilities of white spruce decay resistance to brown rot and white rot were 0.21 and 0.27, respectively. There were no significant differences in resistance to standing-tree-decay fungus among the families. The phenotypic and genetic correlations between the growth rate of brown rot on heartwood blocks and wood density were positive, but the genetic correlation between wood density and the growth rate of white rot on heartwood blocks was negative but not significant. The results indicate that the different species of fungi have different relationships with the annual growth of trees and wood density, and suggest that selection for wood density in white spruce might lead to an increase in resistance to white rot, but a decrease in resistance to brown rot.
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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 | 0.001 |
| 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".