Induction of Flowering by Girdling in Jamun cv. Konkan Bahadoli
Bibliographic record
Abstract
Jamun (Syzygium cuminii) is an underexploited fruit crop gifted with abundant nutritional and medicinal values. In spite of its greater economic value, farmers are reluctant to establish jamun orchards as flowering is the major constraint. The prebearing age of jamun is fairly long even in grafts. It takes about 6 to 7 years for commencement of flowering and many times this period is extended up to 10 years. An experiment was therefore undertaken under two distinct locations having different weather conditions to study the efficiency of girdling for induction of flowering in jamun under Maharashtra conditions. The experiment was conducted in Randomized Block Design with five treatments viz. T1- Deep cut on secondary branches, T2 – Deep cut on tertiary branches, T3- Removal of 3 mm bark on secondary branches, T4- Removal of 3 mm bark on tertiary branches and T5- Control (No girdling). Results indicated that girdling was beneficial in jamun for induction of flowering, greater flowering intensity, more number of flowers and fruits per branchlet, reduced period from flowering to harvesting and higher yield as compared to control plants. Tertiary branches were found to be more appropriate location for girdling than secondary branches. Girdling with deep cut without removal of bark was more beneficial than the removal of bark. T2 was the best treatment of girdling in jamun.
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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 | 0.001 | 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".