The importance of fruit set, fruit abortion, and pollination success in fruit production of teak (Tectona grandis)
Bibliographic record
Abstract
Teak ( Tectona grandis L.) is believed to have been introduced from India 400–500 years ago and there appear to be no old-growth natural forests. However, Indonesia has many teak plantation forests and 40%–50% of seeds for reforestation come from seed orchards and the remainder from plantations. In both, flower and fruit abortion results in low fruit set and thus seed production. We investigated flower and fruit development in a clonal seed orchard in East Java in 1999 and 2001 using cross- and open-pollinated trees. The rates of abortion using cross-pollinations showed a similar pattern but were lower than for open-pollinations. The highest rate of abortion in cross- and open-pollinations was during pollination and fertilization, ranging 11%–23% and 31%–32%/day, respectively, less during fruit initiation (5%–6% and 6%–8%/day) and the lowest was during fruit maturation (<0.5%/day). Cross-pollinations increased fruit production by 10-fold compared with open-pollinations. About 30% of flowers were pollinated with an average of five pollen grains per stigma in open-pollinations. Pollen viability varied among clones and time of day but was not affected by position of flowers in an inflorescence. We conclude that low pollination success and low pollen viability are major causes for low fruit production in teak and are related to insect pollinators.
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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".