Bromatology of Tifton 85 Grass Irrigated with Two Qualities of Water and under Organic Fertilization
Bibliographic record
Abstract
The reuse of solid and liquid wastes has become a viable practice for the sustainability of agricultural production, because it meets the nutritional needs of crops and also allows the use of nutrients present in these residues. The objective of this work was to analyze the nutritional value of Tifton 85 irrigated with treated domestic wastewater and fertilized with nitrogen doses from poultry bed in four successive cuts, with frequency of 35 days. The experiment was set up in a protected environment, arranged in a completely randomized experimental design, in a 5 × 2 factorial scheme, with four replications. Five doses of nitrogen were tested through organic fertilization from avian beds (0, 15, 30, 45 and 60 kg N ha-1) and two irrigation water qualities (treated domestic well and artesian well). After the cuts the material was dried and sent to the laboratory to determine the following parameters: crude protein (CP), neutral detergent fiber (NDF) and Tifton 85 acid detergent fiber (ADF). The highest values of crude protein were obtained using the domestic wastewater treated in the irrigation, as well as using the higher doses of nitrogen from poultry litter. Just as the NDF and ADF contents presented the best values when treated domestic wastewater and the highest nitrogen doses. Indicating that irrigation with wastewater and the use of organic fertilizer may substitute for conventional manures by promoting satisfactory levels of concentrations for fodder.
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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) | 0.000 | 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".