Growth and Micronutrients Contents of Smell Pepper (Capsicum chinense Jac.) Submitted to Organic Fertilizer
Bibliographic record
Abstract
The results of research with smell pepper cultivation are still incomplete and preliminary, especially regarding organic fertilizing and nutritional status. The aims of study were to evaluate the effect of rates of organic fertilizer produced from family agriculture waste on growth and nutritional status of smell pepper. An experiment was conducted in a greenhouse at the Universidade Federal Rural da Amazônia, in Belém city, State of Pará, in period from January to April 2012. The experimental design was completely randomized, with five treatments and four replications, in which each experimental plot being made of a vase with a volume of 3.6 dm3 of soil and a smell pepper seedling. Five rates of organic fertilizer (0%, 15%, 30%, 45%, and 60%) were tested out of the total volume of substrate. The organic fertilizer were formed by mixing chicken manure (10%), duck manure (20%), cassava peel (15%), cassava leaf (15%), bean straw (15%), rice husk (15%), and corn cob (10%). The different amounts of organic fertilizer were mixed in volumetric proportions of substrate of Yellow Latosol, sandy texture, taken from the surface layer (0-20 cm). It was founded that at 103 days, the best results were achieved with a rate of 60% of the organic fertilizer. The content and accumulation of micronutrients in leaf tissue of smell pepper plants followed this descending order: Fe > B > Mn > Zn > Cu, and Fe > B > Mn > Zn > Cu, respectively. In fruits, the content, accumulation, and extraction of micronutrients followed this order: Fe > B > Mn > Zn > Cu.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".