Hyperspectral imaging of grains uncovers the genetic architecture of nitrogen response of development in bread wheat
Bibliographic record
Abstract
Unraveling the genetic architecture of nitrogen response of development is critical for improving wheat productivity while reducing nitrogen inputs. In this study, hyperspectral imaging (HSI) was applied to wheat grains obtained from nitrogen-deficient and normal conditions, combined with genome-wide association studies (GWAS), to investigate the nitrogen response of development in a diverse wheat panel. The 1,792 i-traits were acquired via hyperspectral imaging system, which reflect detailed phenotypic assessments of wheat development, capturing subtle variations in nitrogen response. A total of 3,556 significant loci and 3,648 candidate genes were identified. Key candidate genes involved in nitrogen uptake and utilization were identified by integrating agronomic traits with i-traits, including TaARE1-7A, TaPTR9-7B, TaNAR2.1, and Rht-B1 . This approach underscores the potential of combining HSI on grains with GWAS to dissect complex traits like nitrogen response, offering valuable genetic insights for breeding nitrogen-efficient wheat varieties and enhancing sustainability in crop 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 | 0.000 | 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) | 0.000 | 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".