Data from: High genetic variation and moderate to high values for genetic parameters of Picea abies resistance to Pissodes strobi
Bibliographic record
Abstract
Genetic parameters of Picea abies resistance to the white pine weevil (Pissodes strobi Peck) were estimated from 193 full-sib and 166 half-sib families in six 10-year-old progeny trials. The estimated family and individual heritability values for the cumulative weevil attack rate between ages 6 and 10 (CWA6–10) were high and moderate for both full-sib families (0.61 and 0.28, respectively) and half-sib families (0.85 and 0.40, respectively), indicating strong genetic control for this trait in Norway spruce. The fact that specific combining ability (SCA) variance represents 35 % of the general combining ability (GCA) variance suggests that non-additive (dominant) effects are weak. The strong type B correlations found for CWA6–10 (rˆB=0.96 for full-sib families and rˆB=0.81 for half-sib families) indicate that family ranks were stable across sites. For three of the five sites with high attack levels, genetic correlations were not significant between CWA6–10 and tree height at age 5. Moderate positive genetic correlations were detected between CWA6–10 and tree diameter at age 10 (rˆA from 0.29 to 0.60), but specific families showing both high resistance and good diameter growth can be found. These results suggest that the genetic improvement of Norway spruce for resistance to white pine weevil can be achieved successfully without adversely affecting growth.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".