Exploring the Molecular and Metabolic Factors Contributing to the Adaptation of Maize Seedlings to Nitrate Limitation
Bibliographic record
Abstract
Crop production on soils containing sub-optimal levels of nitrogen (N) severely compromises yield potential. The development of plant varieties displaying high N use efficiency (NUE) will optimize N fertilizer use and reduce the environmental damage caused by excess N application. Maize is one of the most important crops cultivated worldwide. Identification of the genotypes with an enhanced NUE in the field is both time and resource consuming and sometime is difficult due to the regulation in the biotechnology programs. Identification of traits associated with adaptation to N limitation at an early vegetative stage which could reflect NUE at maturity is in need. We developed a hydroponic growth system and used it to test two genotypes that were different in their NUE at maturity under N limitation. One genotype SRG-200 showed a higher NUE than the other genotype SRG-100 and we used its hybrid SRG-150 as a reference for NUE. A number of phenotypic, molecular, and metabolic factors were tested using these three genetic lines at an early vegetative stage to determine which of these could be more indicative of predicting improved NUE at an early seedling stage. These include a transcriptional analysis which showed that the higher NUE in SRG-200 genotype is associated with higher transcript levels for the genes involved in nitrate transport, N assimilation, and GS and that the SRG-200 genotype maintained higher sugar content in leaves. Those identified in this study could be useful indicators for selecting promising maize lines at early stages to help develop elite varieties showing an enhanced NUE.
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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".