Spatial and competition models increase the progeny testing efficiency of Japanese larch
Bibliographic record
Abstract
The main purpose of this study was to examine spatial and competition effects on estimates of genetic parameters, as well as on selection options for growth traits, including height (H), diameter at breast height (DBH), and volume (V), in a progeny test of Japanese larch (Larix kaempferi (Lam.) Carrière) at age 20 years. We compared performances among the individual-tree additive genetic base model (B) with design factors only, the spatial effect model (AR1), the competition model (C), and the combined competition and spatial model (CS). We found that spatial heterogeneity had significant effects on growth traits and that plot variance decreased by more than 80% in the AR1 model relative to the B model. Competition had significant effects on DBH and V but a smaller effect on H. In the C model, direct additive genetic variances ([Formula: see text]) for DBH and V increased by 205% and 93%, respectively, whereas residual variances ([Formula: see text]) decreased by 8% and 6%, respectively. In the CS model, the correlations between direct and competitive genetic effects were 0.83, −0.97, and −0.98 for H, DBH, and V, respectively. Competition significantly affected the forward selection. The proportions of selected elite trees were only 39% and 25% common between the B and CS models for DBH and V, respectively, when selection intensity was 5%. For breeding selection, depending on thinning regimes planned, trees of high additive breeding values but low competitive breeding values are preferable for plantation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".