Genetic Variation in Growth Curve Parameters of Konishii fir (Cunninghamia lanceolata (LAMB.) HOOK. var. konishii)
Bibliographic record
Abstract
Abstract Cunninghamia konishii is the island race of the species complex C. lanceolata, and is native to Taiwan. It is a valuable timber species. A comprehensive provenance- family test was established in 1973. Height and diameter were measured periodically until age 26, which was close to the species’ harvest age of about 30. These data offered an opportunity to examine the species’ growth characteristics by fitting asymptotic growth functions. We adopted the concept of repeated measures data analyses, i.e., a combination of variance component analysis and growth curve fitting, the latter involved fitting the individual tree height and diameter data to a Weibull-based function. A severe typhoon in 1996 caused serious damage to the plantation, mostly to tree heights. To prevent this damage from influencing our results, we limited the analyses to those trees judged relatively free of typhoon damage, and focused on the diameter growth data. Fitting a Weibull function with parameters a, b, and c was statistically successful (e.g. the mean R2 for diameter was 0.98). Both analyses indicate substantial variation among provenances and families, and thus opportunities for genetic selection and breeding. We particularly expound on the practical applications of growth curve fitting as an analytical tool for elucidating the mechanistic process of tree growth to assist decisions on the age for selection, even retrospectively, and modeling the response of tree growth to future climate.
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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 | 0.001 | 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".