Mechanisms and Sources of Resistance in Tropical Maize Inbred Lines to Chilo partellus Stem Borers
Bibliographic record
Abstract
Developing maize with durable resistance to maize stem borers could be enhanced by identifying genotypes with different mechanisms of resistance and pyramiding the resistances into high yielding genotypes. This study was carried out on 120 CIMMYT tropical maize inbred lines to identify the most important mechanisms of resistance that could be used to discriminate the germplasm into resistant or susceptible categories. The experiment was laid in an Alpha-lattice design, and replicated three times during the 2011/12 seasons. Traits measured were leaf toughness, stem penetrometer resistance, trichome density, stem sugar content, leaf damage, number of stem exit holes and stem cumulative tunnel length. A selection index was computed and categorized the 120 inbred lines into 33 resistant, 29 moderately resistant, 31 moderately susceptible and 27 susceptible. The most resistant lines were those derived from the CIMMYT multiple borer-resistant populations with CKSBL10039 being most resistant and CML395 most susceptible with indices of 0.49 and 1.84, respectively. Trichome density, leaf toughness and stem sugar content in that order were the most important traits in discriminating the lines into resistance and susceptible categories. More research is needed to classify the specific types of trichomes and sugars present in both resistant and susceptible inbred lines.
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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 | 0.001 | 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".