Evaluation of Different Organic Fertilizers in the Sustainable Cultive of Coriander
Bibliographic record
Abstract
The excessive use of agrochemicals in agriculture has been causing irreversible environmental impacts, from this point of view, organic farming appears as an economically viable alternative to minimize these impacts. So, the objective of this work was to evaluate the effect of different types organic fertilizers in the development and production of coriander harvested at different epochs. The experiment was conducted during the period from August to October 2016, in an area of the experimental farm of University of International Integration of Afro-Brazilian Lusophony. The experimental design was a randomized complete block design, in a 5 × 4 factorial scheme, consisting of five organic fertilizers and four harvesting epochs (28, 35, 42 and 49 days after planting-DAP) and five blocks. The variables stem diameter, plant height, root size, number of leaves, leaf length and productivity were evaluated. All the analyzed variables responded significantly by the F test, either for the qualitative factor, fertilizer sources, or the quantitative, epochs of evaluation. Regarding the cultivation epochs, when the first evaluation period (28 DAP) was compared with the last (49 DAP), it was verified that the variables presented linear responses over time, with increases of 34.4% for height , 29.5% for stem diameter, 37.07% for root length and 64.44% for production. In relation to the fertilizer sources, in general, the cattle manure provided a greater growth and production of the coriander plants, being therefore the most suitable for the cultivation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".