Genetic effects of rooting loblolly pine stem cuttings from a partial diallel mating design
Bibliographic record
Abstract
More than 239 000 stem cuttings from nearly 2200 clones of loblolly pine (Pinus taeda L.) were set in five rooting trials to estimate genetic parameters associated with rooting. Overall rooting success across the five trials was 43%, and significant seasonal effects were observed. Differences among clones within full-sib families accounted for approximately 10%17% of the total variation. On the binary scale, individual-tree narrow-sense heritability (ĥ20.1) ranged from 0.075 to 0.089 for rooting across the five separate settings, while broad-sense heritability (Ĥ20.1) ranged from 0.15 to 0.22. Narrow- and broad-sense heritability estimates on the observed binary scale were transformed to their underlying normal scale (ĥ2N, Ĥ2N). When all of the data from the five trials were analyzed together, ĥ2N(±SE) was 0.081 (0.027), Ĥ2Nwas 0.16 (0.013), the type B additive correlation was 0.68 (0.23), and the type B dominance correlation was 0.61 (0.27). Narrow-sense family mean heritability was 0.83 (0.24), while broad-sense clonal mean heritability was 0.82 (0.074). These moderate to high family and clonal mean heritabilities, moderate type B correlations, and substantial among-family and among-clone genetic variation indicate the potential for increasing rooting efficiency by selecting good rooting families and clones or culling poor rooters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".