Bibliographic record
Abstract
Heat stress remains a major environmental factor which decreases the yield and productivity of most cereals growing worldwide.The research work was performed to analyze the influence of heat stress on the performance of morpho-physiological characteristics of Triticum aestivum genotypes at NARC Islamabad, Pakistan during 2014 -2015.Various traits of wheat were evaluated by using complete randomize design with triplicates.Analysis of variance reveals adverse influence of heat stress on observed traits of selected genotypes.The result indicates that extreme temperature causes reduction to grain yield, yield per plant.All genotypes responded different against heat stress as compared to optimum temperature.Among all varieties, genotypes 1067, 1123, 1124, 1137, 1154, 1159 and 1163 confirmed most tolerable to heat stress regarding seeds yield per plant, proline content, membrane stability index and total chlorophyll contents.It is suggested that heat stress tolerant varieties should be used in plant breeding programs in the development of different wheat varieties having heat stress tolerance at different stages of plant growth and further research studies should be investigated for the progress of potential heat acceptable genotypes in high temperature regimes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.942 | 0.926 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".