Seed Microbiolization Associate With Nitrogen Doses Increase the Nutrition of Tomato Fruits
Bibliographic record
Abstract
Endophytic bacteria can promote growth and improve the quality of plant production. The objective of this work was to evaluate the efficiency of inoculation of a mix of non-host endophytic bacteria isolates in the nutrition of tomato fruits cultivated, fertilized with rock powder and different doses of nitrogen. The experimental design was in randomized blocks, in a factorial arrangement (2 × 6 + 3), with four replications. The treatments consisted of two methods of inoculation of the mix of endophytic bacteria: seed microbiolization and post-emergence inoculation; six doses of nitrogen fertilization: 0, 129, 258, 387, 516 and 645 kg ha-1; and 3 controls (without inoculation of the bacterial mix). The average export of macro and micronutrients in tomato fruits was, in descending order: K > N > P > Ca > Mg > S (averages of 1.33; 0.46; 0.07; 0.06; 0.05 and 0.05 g plant-1, respectively) and Mn > Cu > Fe > Zn (means of 2.67; 1.98; 1.71 and 0.79 mg plant-1, respectively). The inoculation method by seed microbiolization associated with nitrogen doses promoted significant increment in the dry mass of the fruits and in the content of the nutrients P, Ca, Zn, Fe and Mn.
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 | 0.000 |
| 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".