Genetic parameters and correlations between stem size, forking, and flowering in teak (<i>Tectona grandis</i>)
Bibliographic record
Abstract
The genetic improvement of teak (Tectona grandis Linn. f.), a high value tropical hardwood, has been hindered by a paucity of genetic parameter estimates. In particular, an association between flowering age and forking height has been suggested but never before quantified. In this study, 3- to 6-year data from a cloned progeny test were used to estimate heritability and genetic correlations among stem size, forking, and flowering traits. Mean narrow-sense heritability estimates [Formula: see text] were 0.09–0.10, and mean broad-sense heritability estimates ([Formula: see text]) were 0.38–0.45 for stem size traits. There were no age trends in [Formula: see text] or [Formula: see text]. Age–age additive and nonadditive genetic correlations were strong and were not related to the time interval between measurements. Forking height and forking age were under weak to moderate genetic control. Flowering age was under substantially greater genetic control, with [Formula: see text] of 0.21 and [Formula: see text] of 0.46. Additive and nonadditive genetic correlations between forking height and flowering age were estimated to be 0.84 and 0.55, respectively. Improvement of forking height was calculated to be almost twice as efficient by indirect selection on late flowering. These results suggest that within-provenance selection for teak stem size need not be delayed beyond 3 years and that indirect selection on flowering age will improve forking height.
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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 | 0.001 |
| 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".