Field growth performances of teak genotypes of different ages clonally produced by rooted cuttings, in vitro microcuttings, and meristem culture
Bibliographic record
Abstract
Teak (Tectona grandis L. f.) clonal forestry has lately become a reality thanks to the development of efficient techniques for mass clonally propagating true-to-type teak trees of various ages. Field trials were set up to assess the influence of teak genotypes of different ages and three clonal propagation techniques on field growth performances of teak clones. Significant differences (P < 0.0001) in height (H, from 11.9 to 17.5 m), diameter at breast height (D, from 11.8 to 18.9 cm), and volume (V, from 67.9 to 194.7 dm3) were observed 5 years after planting for clones produced by microcuttings from 6-month-old to 70-year-old teak ortets, regardless of their age. After 6.5 years of testing, H, D, and V performances of clones produced by rooted cuttings and microcuttings from 7-year-old teak trees were similar, notwithstanding clone × propagation method interactions. Five years after planting, clones produced by meristem culture from 7-year-old ortets had larger diameters and volumes than clones produced by microcuttings, whereas H varied according to clone × propagation method interaction. The various propagation methods used had no significant effect on mortality (<10%). The pros and cons of these techniques for mass clonally propagating teak genotypes of different ages were discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".