Water-conducting roots responsible for nitrogen uptake in maize ( <i>Zea mays</i> )
Bibliographic record
Abstract
Abstract Nitrate (NO 3 - ) uptake is primarily driven by mass flow and varies among maize root types. The importance of embryonic and crown roots in acquiring NO 3 - was determined here in wet and dry soils. Maize was grown in a split-root pot that segregated the embryonic and crown roots. The soil was moistened to water potentials of either -5kPa or -30kPa. A partial N mass balance was made by destructively sampling shoots, roots, and soils after 0, 24, and 48 h following 15 N-KNO 3 injection at the V3 and V6 stages. Gross nitrification was assessed using a 15 N isotope dilution technique. At the V3 stage, crown roots had 202% more N uptake than embryonic roots in wet soil (-5 kPa). However, in dry soil (-30 kPa), N uptake was similar for embryonic and crown roots, possibly due to an 80% reduction of hydraulic conductance in crown roots. By the V6 stage, crown roots dominated N uptake, with embryonic roots supplying < 20% of N uptake. Soil gross nitrification rate was similar for root types. The present studies indicated that maize NO 3 - uptake depends primarily on the crown roots, due to their capacity to extract water and NO 3 - from soil, even under dry conditions. Highlight This study reveals how different maize root types contribute to nitrate uptake under varying soil moisture conditions, suggesting that soil water management is important to ensure optimal nitrogen uptake .
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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) | 0.001 | 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".