Novel temporal, fine‐scale and growth variation phenotypes in roots of adult‐stage maize (<i>Zea mays</i> L.) in response to low nitrogen stress
Bibliographic record
Abstract
There is interest in discovering root traits associated with acclimation to nutrient stress. Large root systems, such as in adult maize, have proven difficult to be phenotyped comprehensively and over time, causing target traits to be missed. These challenges were overcome here using aeroponics, a system where roots grow in the air misted with a nutrient solution. Applying an agriculturally relevant degree of low nitrogen (LN) stress, 30-day-old plants responded by increasing lengths of individual crown roots (CRs) by 63%, compensated by a 40% decline in CR number. LN increased the CR elongation rate rather than lengthening the duration of CR growth. Only younger CR were significantly responsive to LN stress, a novel finding. LN shifted the root system architectural balance, increasing the lateral root (LR)-to-CR ratio, adding ∼70 m to LR length. LN caused a dramatic increase in second-order LR density, not previously reported in adult maize. Despite the near-uniform aeroponics environment, LN induced increased variation in the relative lengths of opposing LR pairs. Large-scale analysis of root hairs (RHs) showed that LN decreased RH length and density. Time-course experiments suggested the RH responses may be indirect consequences of decreased biomass/demand under LN. These results identify novel root traits for genetic dissection.
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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 | 0.001 |
| 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".