Selecting for stable and productive families of <i>Eucalyptus urophylla</i> across a country-wide range of climates in Brazil
Bibliographic record
Abstract
To identify stable and productive Eucalyptus urophylla S.T. Blake families across diverse climate zones in Brazil, we evaluated growth and survival of 322 open-pollinated families derived from 13 genetically improved seed sources in 10 trials across the country. Survival and growth data were analyzed using linear mixed models and REML/BLUP. Survival ranged from 51% to 92%, and the mean annual increment varied from 19 to 46 m3·ha−1·year−1. Although planted in suitable climatic zones, some trials had low survival and (or) productivity. Conversely, the highest productivity was recorded in a zone considered to be of low suitability. These results show the importance of assessing the climatic requirements of eucalypts beyond those determined from analyses of their natural distribution, especially when testing already improved seed sources. A number of productive and stable families were identified based on analysis of the interaction between genotype and environment, and from these, 144 individuals were selected and had their genetic diversity estimated using 19 microsatellite DNA markers. The genetic diversity of these selected trees was equivalent to that observed in previous studies of natural populations of E. urophylla, indicating that breeding programs of E. urophylla in Brazil still retain high levels of diversity for sustainable genetic gains.
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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 | 0.000 |
| 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) | 0.000 | 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".