Characterization of a Spring Wheat Association Mapping Panel for Root Traits
Bibliographic record
Abstract
Improved root traits are important for increased nutrient and water uptake and productivity in wheat (Triticum spp.). The objectives of this research were to characterize genetic variability for root traits in a Spring Wheat Association Mapping (AM) Panel and determine whether root traits are related to shoot dry weight, tiller number, and plant height. Rooting depth, root dry weight, root/shoot ratio, and shoot traits were determined for 250 genotypes of the AM panel. The remaining root traits were measured for a subset of 30 genotypes selected based on rooting depth. Significant genetic variability was observed for root traits. Genotypes Treasure and IDO686 were ranked high and genotypes MN08106‐6 and MT1016 were ranked low for most root traits in the AM panel or its subset. Shoot dry weight had positive relationships (correlation coefficient, r ≥ 0.50) with rooting depth and root dry weight in the AM panel, and with total root length, total root surface area, root length density (30–60‐cm soil depth), fine root length, and fine root surface area in the subset. Tiller number also had positive relationships (r ≥ 0.50) with all the above root traits except rooting depth and root dry weight. Plant height had no correlations with most root traits. Plant height had only weak negative relationships (|r| ≤ 0.36) with root dry weight and root/shoot ratio in the AM panel. The genetic variability identified in this research for root traits offers useful information for wheat improvement programs for choosing genotypes with contrasting root characteristics.
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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 | 0.001 |
| 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".