Impact of Irrigation on the Yield, Total Cost, and Its Influence on Hydraulic Conductivity in a Soil Under Vinasse Application
Bibliographic record
Abstract
This study had the objective to evaluate the effect of irrigation and fertigation (N) in the crop yield and its impact on the total cost, in addition to its influence on the conductivity hydraulic capacity and logarithmic hydraulic conductivity in an Oxisol under vinasse application cultivated with sugarcane (second year), in state of Goiás, Brazil. The experimental design comprised randomized blocks in a 5 × 2 factorial scheme, with four replications. Treatments consisted of five levels of water replacement (100, 75, 50, 25 and 0%), with and without fertirrigation (N). The planting of sugarcane, cultivar RB85-5453, was performed in a double row (W-shaped), 8 m long, with 1.80 m spacing between the double rows, the distance between the crops in the double row was 0.40 m, with a total area of 52.8 m2 in each paddock. For treatments with water, replacement (WR) a drip tube was placed in the ground at a depth of 0.20 m among the furrows of the double row. The drip tube (DRIPNET PC 16150) comprised a thin wall, 1.0 bar pressure, nominal discharge 1.0 L h-1, and 0.50 m spacing between drippers. Nitrogen was applied by fertirrigation at a dose of 100 Kg ha-1, at 30-day intervals, with 10 applications throughout the development of the sugarcane culture. Potassium fertilization was fully realized at planting. The total cost with irrigation did not increase significantly with fertirrigation (< 3%) in an Oxisol under vinasse application.
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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 | 0.001 | 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".