Genetic control of <i>Eucalyptus globulus</i> harvest traits
Bibliographic record
Abstract
The cost of harvesting short-rotation plantation eucalypts can be in excess of AU$2500·ha−1. Despite this high cost, the extent to which harvesting productivity is affected by tree genetics is not well understood. We address this issue in a study of two 10-year-old genetic field trials of Eucalyptus globulus Labill. in Australia. Standing-tree traits analysed were survival, diameter at breast height, basal area, and stem straightness and forking. Harvest traits analysed were volume, time, and productivity. Genetic group and within-group genetic variation (additive and dominance), stand-level family variation, phenotypic and genetic correlations, and the effects of inbreeding were estimated for these traits. The different scenarios studied showed that plantation harvest productivity was affected by tree genetics to some degree but was mainly affected through positive covariation with stem diameter. Harvest productivity is thus unlikely to have been adversely affected by past selection. Although no significant additive or dominance genetic variation in stem forking or straightness was detected, weak phenotypic correlations were consistent with harvest productivity being higher in straighter trees with no forking. High inbreeding depression was evident for growth and survival; however, in open-pollinated progeny, this resulted in only a slight reduction in harvest productivity (5.5%) compared with out-crossed progeny.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".