Characterization of Elite Upland Cotton Genotypes for Earliness and Yield Traits
Bibliographic record
Abstract
Short duration cotton genotypes increased the chances to harvest crop before cold and rainy weather conditions. However, earliness in cotton is a difficult character, which is assessed by determining many plant traits. The current experimental was conducted at Cotton Section, A.R.I. Tando Jam, during kharif season 2013. The eight cotton genotypes including CRIS-342, Sindh-I, Haridost, Malmal, Bt-121, Bt-3701, TS-501 and Shahbaz were sown i n randomized complete block design with four replications. In a total, seven quantitative traits were measured such as days to first flowering, node number to set first sympodial branch, node number to set first flowering, sympodial branch length (cm), sympodial branches plant-1, number of bolls plant-1, seed cotton yield plant-1(g). The mean squares from analysis of variances showed that genotypes differed significant (P 0.01) for all the studied traits, demonstrating a vast genetic variability in tested genotypes. The genotypes, Sindh-1, CRIS-342, Bt-121 and Bt-3701 were characterized as early maturing or short duration cotton varieties. However, the correlation between yield and earliness traits were best criteria for developing the short duration cotton yield varieties.
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 | 0.000 | 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 | 0.000 |
| 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".