Initial productivity and genetic parameters of three <i>Corymbia</i> species in Brazil: designing a breeding strategy
Bibliographic record
Abstract
The survival, initial productivity, and the genetic parameters of Corymbia citriodora subsp. citriodora (CCC), Corymbia citriodora subsp. variegata (CCV), and Corymbia torelliana (CT) were used to develop a breeding strategy for the Corymbia species. Survival, height, and diameter at breast height (DBH) data were assessed 24 and 36 months after planting, and the mean annual volume increment was estimated in three trials. Longitudinal DBH data analysis was applied individually to each trial to identify the best and the poorest families at both ages. The mortality ranged from 5% in CT to 27% in CCC, and the mean annual increment varied from 17.8 to 20 m3·ha−1·year−1 at 36 months after planting. The 36-month narrow-sense heritability [Formula: see text] was high for CCV (0.69 ± 0.17), moderate for CCC (0.41 ± 0.11), and low for CT (0.21 ± 0.09). The genetic parameters indicated the need for different breeding strategies for each species. Selecting the best families while roguing the poor families allowed forward selection of CCC and CCV. It was possible to select good CT trees for hybrid breeding; however, improving the species population requires focusing on increasing the effective size and expanding the genetic variability in the CT population.
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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 | 0.000 | 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".