CPPU and salicylic acid application improved fruit retention, yield, and fruit quality of mango cv. Dusehri
Bibliographic record
Abstract
The present investigation was aimed at assessing the efficacy of N1-(2-chloro-4-pyridyl)-N3-phenylurea (CPPU) and salicylic acid on fruit retention, yield, and quality of mango cv. Dusehri. The research was carried out at the Department of Fruit Science, Punjab Agricultural University, Ludhiana, India. The experiment was conducted simultaneously at two different locations for two cropping seasons during 2019–20 and 2020–21. Fruit retention enhancing treatments of 2,4-Dichlorophenoxyacetic acid (2,4-D) (@20 ppm), CPPU (@5, 10, and 15 ppm), and salicylic acid (@100, 200, and 300 ppm) were applied at pea stage of fruit growth. Experimental plants were observed for various reproductive, yield, and fruit quality parameters. The results indicated that foliar application of CPPU (T6-CPPU @10 ppm) significantly enhanced fruit retention during marble and harvest stages at both locations in both seasons. The novel growth hormone also improved fruit yield, fruit weight, and fruit quality in mango cv. Dusehri. Foliar applied salicylic acid recorded intermediate values for observed parameters. Therefore, foliar application of CPPU can be considered as a better alternative to 2,4-D for fruit drop management of mango.
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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".