Genetic analysis of wood quality traits in Norway spruce open-pollinated progenies and their parent plus trees at clonal archives and the evaluation of phenotypic selection of plus trees
Bibliographic record
Abstract
A two-generation pedigree involving 519 Norway spruce (Picea abies (L.) Karst.) plus trees (at clonal archives) and their open-pollinated (OP) progenies was studied with the aim to evaluate the potential of plus-tree selection based on phenotype data scored on the plus trees. Two wood properties (wood density and modulus of elasticity, MOE) and one fiber property (microfibril angle, MFA) were measured with a SilviScan instrument on samples from one ramet per plus tree and 12 OP progenies per plus tree (total of 6288 trees). Three ramets per plus tree and their OP progenies were also assessed for Pilodyn penetration depth and Hitman acoustic velocity, which were used to estimate MOE. The narrow-sense heritability (h2) estimates based on parent–offspring regression were marginally higher than those based on half-sib correlation when three ramets per plus tree were included. For SilviScan data, estimates of the correlation between half-sib, progeny-based breeding values (BVs) and plus-tree phenotypes, as well as repeatability estimates, were highest for wood density, followed by MOE and MFA. Considering that the repeatability estimates from the clonal archive trees were higher than any h2 estimate, selection of the best clones from clonal archives would be an effective alternative.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".