Foliar feeding of boron improves the productivity of cotton cultivars with enhanced boll retention percentage
Bibliographic record
Abstract
Cotton (Gossypium hirsutum L.) is of prime importance because of its quality fiber and edible oil production. Boron (B) is among essential micronutrients for plant growth; it aids in the transfer of sugars and nutrients from leaves to fruit that are involved directly or indirectly in many plant functions. Cotton growth, yield and quality are strongly affected with boron application. A two-year study was conducted to evaluate the impact of foliar applied B (0, 2, 4, 6, 8 and 10 g of B L−1 of water) on the performance of cotton cultivars (FH-113, MNH-786 and CIM-496). The results indicated that growth, yield and quality traits of cotton were significantly influenced by different levels of foliar applied boron as well as cultivars of cotton. Among cotton cultivars, the yield and quality parameters were superior in cultivar “FH-113.” Foliar application of boron at 6 g L−1 of water improved leaf area index and leaf area duration and eventually improved the number of bolls per plant, boll retention percentage, average boll weight, lint yield, ginning out turn, fiber length and uniformity ratio of cotton. Foliar application of B at 6 g per liter of water, showed promising results by improving growth and quality parameters and is recommend to enhance the economical yield production of cotton cultivar “FH-113” with improved quality.
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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".