NH<sub>4</sub><sup>+</sup> -N/NO<sub>3</sub><sup>−</sup> -N ratios on growth and NO<sub>3</sub><sup>−</sup> -N remobilization in root vacuoles and cytoplasm of lettuce genotypes
Bibliographic record
Abstract
Wang, B. and Shen, Q. R. 2011. NH4+ -N/NO3− -N ratios on growth and NO3−-N remobilization in root vacuoles and cytoplasm of lettuce genotypes. Can. J. Plant Sci. 91: 411–417. Aerobically growing crops generally prefer nitrate (NO3−) to ammonium (NH4+), but partial replacement of NO3− by NH4+ usually makes crops grow better. Five cultivars of lettuce (Lactuca sativa L.) frequently cultivated in southeast China, including Ny, Sx1, Rw, Nct and Nrnct, were hydroponically grown in four ratios of NH4-N:NO3-N: 0:100, 10:90, 25:75 and 50:50. Based on biomass of roots and shoots of plants, Sx1 was a mostly sensitive genotype, while Nrnct was a mostly insensitive genotype to partial NO3− replacement by NH4+ among the five cultivars. Total root surface area, root length, SPAD readings and net photosynthesis rate of Sx1 and Nrnct were found to be highest at 25:75 of NH4-N:NO3-N. Sx1 had a greater improvement than Nrnct under moderate NH4+ nutrition (NH4-N:NO3-N=25:75). The capacities of NO3− release and remobilization from vacuoles to cytoplasm of Sx1 were stronger than those of Nrnct. The results suggest that we can improve the utilization of nitrogen and decrease the NO3− content in lettuce through planting the appropriate lettuce cultivar in a proper NH4-N/NO3-N ratio.
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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) | 0.001 | 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".