Analysis of Residential Irrigation Distribution Uniformity
Bibliographic record
Abstract
Irrigation has become commonplace for residential homeowners desiring high quality landscapes in Florida. The goal of this project was to document irrigation system uniformity in Central Florida and to quantify distribution uniformity of residential sprinkler equipment under controlled conditions. The catch-can testing procedure used was a modified version of both the American Society of Agricultural Engineers standard and Florida Mobile Irrigation Laboratory (MIL) procedures. The modified version included a larger sample size to ensure complete sample collection over the entire irrigated area. The standard MIL procedure may overestimate the uniformity for residential systems. From the tests on residential irrigation systems, the average low quarter distribution uniformity (DUlq) value was calculated as 0.45. Rotary sprinklers resulted in significantly higher DUlq compared to fixed pattern spray heads with 0.49 compared to 0.41, respectively. From uniformity tests performed on rotor and spray heads under ideal conditions, rotor heads had more uniform distributions than the spray heads of 0.55 compared to 0.49, respectively. Spray heads had better uniformity when fixed quarter circle nozzles were used as opposed to adjustable nozzles. Both residential irrigation system and controlled tests resulted in (DUlq) at the low end of industry guidelines. Residential irrigation system uniformity can be improved by minimizing the occurrence of low pressure in the irrigation system and by ensuring proper spacing is used in design and installation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".