Fertilization With Laying Hen Manure and Economic Analysis in Caesar Weed (Urena lobata L.) Seed Production in Amazonas, Brazil
Bibliographic record
Abstract
The Caesar weed (Urena lobata L.), produces a light-colored fiber used in the textile industry for the production of sacks, fabrics, and rugs. The bottleneck in the Caesar weed production chain is the seed production. Because Caesar weed grows in a floodplain for fiber production, it does not complete its growth cycler and produce seeds. Therefore, the seeds used in the state of Amazonas come from the Brazil state of extractivism in Pará, which increases the seed costs for fiber production. It is advantageous to develop production technologies that will produce large quantities of viable Caesar weed seed on land in the state of Amazonas. Fertilizer management is an essential element to the successful crop and seed production. Laying hen manure as an organic fertilizer is one of the most accessible fertilizers for the family farmer. It is produced in large volumes at a low cost. The purpose of this research was to evaluate the impact of different doses of laying hen manure on the production of Caesar weed seeds from the perspective of an economic analysis. The experiment was a randomized complete block design with five doses of laying hen manure (0, 5, 10, 15, 20 t/ha) and four replications. Seed productivity was evaluated from the economic point of view of fertilization. A dose of 12.7 t/ha of laying hen manure is recommended for the production of 890 kg/ha of Caesar weed seeds in low fertility soil with a very clayey texture (Souza, 2012).
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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 | 0.001 |
| 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".