Nitrogen Sources and Doses in Arugula Development
Bibliographic record
Abstract
Leafy vegetables have a high demand for nitrogen availability; however, excessive nitrogen supply causes economic, environmental and agronomic losses, compromising food security. Given the above, the objective was to assess the agronomic responses of arugula that are associated with different nitrogen sources and doses. The experiment was run under greenhouse conditions. A randomized block design was employed; the blocks were arranged in a factorial scheme (2 × 4), using two sources (urea and calcium nitrate) and four nitrogen doses (0, 40, 120 and 360 mg kg-1), with four replications. Thirty-five days after transplanting, the following were assessed: plant height, number of leaves, shoot fresh mass, root fresh mass, shoot dry mass, root dry mass, shoot/root dry matter ratio, leaf area, and leaf nitrogen content. It was found that nitrogen fertilization optimizes crop development and yield. Doses of 100 to 272 kg ha-1 promote increase in plant height and leaf number, respectively. Under the conditions studied, 200 kg ha-1 of N is recommended as a dose of maximum economic efficiency in arugula production. Calcium nitrate is indicated as the best nitrogen source for the production of the crop.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".