Identification of early-maturing maize inbred lines based on multiple traits under drought and low N environments for hybrid development and population improvement
Bibliographic record
Abstract
Badu-Apraku, B. and Akinwale, R. O. 2011. Identification of early-maturing maize inbred lines based on multiple traits under drought and low N environments for hybrid development and population improvement. Can. J. Plant Sci. 91: 931–942. Drought and low nitrogen stresses are major limiting factors to maize (Zea mays L.) production and productivity in West and Central Africa. Studies were conducted from 2007 to 2009 at three locations in Nigeria under induced drought stress and low nitrogen conditions. The objective was to identify superior inbred lines for use as parents for hybrid production and for introgression into maize breeding populations. The inbreds TZEI 17, TZEI 13, TZEI 23, TZEI 2, TZEI 3, TZEI 22, TZEI 7, TZEI 11, and TZEI 8 were identified as the most promising parents under drought stress. Under low N, TZEI 7, TZEI 11, TZEI 2, TZEI 4, TZEI 10, TZEI 8, and TZEI 22 were selected. TZEI 11, TZEI 2, TZEI 8, and TZEI 22 had combined tolerance to drought stress and low N and could be used as germplasm sources for introgression of tolerance genes as well as for the development of drought and/or low-N-tolerant hybrids. Under drought stress, TZEI 17, TZEI 3, TZEI 23, and TZEI 13 were the closest to the ideal genotype, while TZEI 7, TZEI 2, and TZEI 11 were the closest under low-N conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".