The Effect of Heterogeneity Due to Inappropriate Tillage on Water Advance and Recession in Furrow Irrigation
Bibliographic record
Abstract
In surface irrigation, uniformity of longitudinal slope and depth of tillage are very important factors in advance duration time. Inappropriate tillage and land preparation can cause uneven surfaces and non-uniform slopes in fields which is especially important in furrow irrigation due to their influence on advance times. The purpose of this study is to evaluate the effect of non-uniform longitudinal slope due to inappropriate tillage on advance and recession phases in furrow irrigation system, which plays an important role in irrigation uniformity and application efficiency. For this purpose 12 furrows, each 42 m long were made with different longitudinal slopes and a width of 0.6 meter. The number of irrigation was 5 with irrigation interval of 4 days and input discharge of 0.8 lps. Results showed that advance times are very different in furrows especially in the first irrigation and varied from 19min up to 50min. The values for recession time were 9 to 29 min. The results of these five irrigation events indicate that non-uniform slope has significant effect on advance and recession time. Therefore with attention to the water crisis and the need to increase efficiency and uniformity of water distribution in surface irrigation, using modern machinery and paying more attention to accurate preparation of land are necessary in order to obtain uniform furrows in fields.
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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 | 0.001 |
| 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".