Genetic improvement of<i>Eucalyptus grandis</i>using breeding seedling orchards and multiple population breeding strategy in Zimbabwe
Bibliographic record
Abstract
Synopsis Eucalyptus grandis is commercially important in Zimbabwe and a breeding program has been in progress since 1962. A classical breeding strategy was used initially but, in 1981, the Multiple Population Breeding Strategy (MPBS) was implemented and the concept of the Breeding Seedling Orchard (BSO) became central to the MPBS in Zimbabwe. Two-year height data from five BSOs established at Mtao and Mukandi in Zimbabwe over two successive generations under the MPBS were used to determine the extent of genetic gain and the implications for future breeding strategy. Four genetic checks, three of which were common to all BSOs, were included by which to monitor the possible onset of inbreeding and against which to measure genetic gain. Significant differences between families were detected for height in the third generation BSOs but no significant differences were detected in one-fourth generation BSO. Genetic checks had average ranking at Mtao where they were selected but had very low ranking at Mukandi. Ranking among genetic checks was fairly similar in the BSOs at Mtao, suggesting that the design ofthe BSOs is satisfactory for producing a ranking offamilies at two years at this site. However ranking among the genetic checks at Mtao differed from those at Mukandi. Genetic correlation between height at two years and volume at age five years was favorable (0,81) suggesting that height at two years is a good predictor of volume at five years. There was no genetic progress in second year height in the fourth generation compared to third generation BSOs. To ensure gain in advanced BSOs,larger numbers of families should be included in the BSOs and a Best Linear Unbiased Prediction methodology should be used for selecting candidates for advanced breeding.
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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".