Genotype by Trait Associations among Drought Tolerant Maize Inbred Lines
Bibliographic record
Abstract
Twelve tropical, yellow maize inbred lines identified as drought tolerant were evaluated in multi environments, including managed drought, rain fed and irrigated conditions. The objective was to study genotype-trait associations across environments. A 3 × 4 a-lattice design with two replications was used in each environment. Data were recorded for twenty-one traits. Combined analysis of variance using data from all environments was done for all traits using the GLM procedure in SAS version 9.3. Genotype by trait associations were revealed using the genotype main effect plus genotype-by-environment biplot model in GENSTAT 14th Edition. Inbred lines which were associated with high grain yield and related desirable traits such as a low drought susceptibility index under managed drought were DMR-M-81, DMR-M-88, FA6, GPM36 and M39. Across the diverse environments, DMR-M-84, DMR-M-88, FA6 and GPM36 were associated with grain yield and/or its related traits. The inbred lines associated with desirable traits could be evaluated for combining ability in order to know their desirability in cultivar development. These inbred lines could be used as female parents in seed production programmes since high productivity and drought tolerance are important qualities of female parents in seed production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".