Mitigation of adverse effects of heat stress in chillies by using glycine betaine
Bibliographic record
Abstract
Chilli (Capsicum spp.) is an important vegetable cum spice crop of the night shade family requiring 20-30 °C optimum temperatures for plant growth and development, usually growth starts retarding below 15°C or above 32°C temperature. Almost all growth stages of chilli plants are influenced by high temperatures ultimately leads to economic yield losses in final crop productivity. The experiment was carried out in growth chamber of mushroom lab, Institute of Horticultural Sciences, University of Agriculture, Faisalabad aiming at identifying the best glycine betaine treatment proved to be useful in coping with adversaries of high temperature stress in chillies. Chilli genotypes named as C-37, Uk-101, H-13 and jawala were grown and sprayed with different concentrations (0, 5, 10, 15 and 20 mM) of glycine betaine at the seedling stage under high temperature stress (40/32ºC day and night temperature) in growth chamber provided with controlled conditions. Various physiological attributes of chilli genotypes were recorded. Glycine betaine application @15mM was best for enhancing the heat tolerance potential of chilli genotypes under heat stress. Glycine betaine has also been proved effective in enhancing the heat tolerance potential under high temperature stress.
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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".