Exogenous Polyazole (PP<sub>333</sub>) Regulated Flower Physiology to Promote Early Bud Extraction of Pisang Awak (ABB)
Bibliographic record
Abstract
In this study, the first and second harvesting Pisang Awak in the field were sprayed with different dosage of paclobutrazol in order to analyze the mechanism of its induction of flowering. The results showed that there was no significant difference in the total number of newly extracted leaves between treatments. The first harvesting was 3.0 g/plant, and the second one was 2.0 g/plant. The pumping speed of leaves is the fastest and the accumulation of leaves number is the earliest. The 3.0 g/plant treatment of first harvesting Pisang Awak had the earliest bud extraction stage, and the bud extraction rate reached 62.5% at 170 days after treatment, about 40 days earlier than control group. Within the range of 1.0~5.0 g/plant, PP 333 promoted carbohydrate synthesis in leaves of Pisang Awak, significantly reduced the accumulation of nitrogen, significantly increased the carbon-nitrogen ratio (C/N), and significantly reduced the contents of GA 3 and IAA in the leaves. The results showed that the exogenous polyazole could accelerate the pumping speed of leaves, promote flowering and early bud extraction by regulating the distribution of carbon and nitrogen nutrients and the content of endogenous hormones, thus providing technical guidance for the management of early bud extraction culture of Pisang Awak.
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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 | 0.001 |
| 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".