Genetic variation in yield of five hybrids of sweet corn grown under poultry manure and nitrogen fertilizers and the presence of the nitrate reductase gene (Nia2)
Bibliographic record
Abstract
Concern about nitrate (NO3−) accumulation in plants and its hazard to human and animal health has led to the investigation of the genetic variation in its accumulation in plants.Genetic variation in the productivity and nitrate content of sweet corn hybrids (Zea mays L.) when produced under five treatment combinations of chemical nitrogen fertilizer and poultry manure was investigated. In addition, the presence of the nitrate reductase gene (Nia2) in hybrids of sweet corn was investigated by PCR analysis. The chlorophyll content of leaves was higher with chemical fertilizer and the mixture of chemical fertilizer and chicken manure compared with chicken manure only. The highest grain yield was recorded in the hybrid Amera grown with chicken manure or the mixture of nitrate fertilizer and poultry manure. Moreover, gene-specific primer pairs for amplification of nitrate reductase revealed the presence of the nitrate reductase gene (Nia2) in hybrid Merit, which had the lowest grain nitrate content. Moreover, Merit only had one extra band (900 bp) indicating the Nia2 gene was controlled by co-dominant alleles. However, the presence of nitrate reductase gene (Nia2) alone did not explain nitrate content differences among corn hybrids. The work presented in this paper showed that PCR assays represent a sensitive tool for screening of sweet corn breeding material for the Nia2 gene although the presence of this gene does not alone explain nitrate content. Key words: Genetic variation, Low nitrate concentrations, sweet corn, nitrate reductase gene (Nia2), PCR
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".