Genetic parameters and genotype–environment interactions of Chinese fir (<i>Cunninghamia lanceolata</i>) in Fujian Province
Bibliographic record
Abstract
Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is the most commercially important conifer in China, and the Nanjing Forestry University – Fujian Province Chinese fir Cooperation (NJFU – Fujian Cooperation) breeding program has advanced it into the third cycle of selection and breeding. In this paper, we estimated genetic parameters from four sites for 80 half-sib families and summarized previous estimates of genetic parameters in Chinese fir with an objective to propose optimal breeding strategy. Heritability averaged 0.20 and 0.14 for height and diameter at breast height (DBH), respectively, for the four sites. A significant genotype–environment interaction (G × E) for growth was also observed among the four sites, with the greatest interactions between a marginal site and the three central sites in the Fujian Province Chinese fir plantation region. The average estimated type-B genetic correlation between the marginal site and the three central sites was 0.08 for height and –0.09 for DBH. However, the highly productive families were among the most stable across the four sites. The results from this study in combination with summarized genetic parameters from literature were used to discuss and propose an optimal breeding strategy for the third generation of the breeding program for Chinese firs in Fujian Province.
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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 | 0.001 | 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".