Application of the steady-state principle of constant-head well permeameter to indirect subsurface drip irrigation
Bibliographic record
Abstract
Indirect subsurface drip irrigation (ISDI) is a method of increasing the irrigation water use efficiency of drip irrigation without the need to bury irrigation tubes and wet the soil surface. A major problem of ISDI is the mismatch between emitter discharge rate and water-conducting device dimension, which will result in over-filling of application water. In this paper, we propose to use the steady-state principle of constant-head well permeameter (CHWP) to quantify the relationship between emitter discharge rate and water-conducting device dimension for ISDI. CHWP tests and ISDI tests were carried out in a 300 m2 winter wheat fallow to verify its feasibility. The steady-state characteristic of these two methods was also studied using long-term infiltration. Results indicate that the equilibration time (110 min) in the ISDI tests was greater than that in the CHWP tests (30 min). The steady ponded depth in ISDI had a smaller variation than the steady water discharge rate in the CHWP. When using the steady-state principle of CHWP to design ISDI systems, there was significant linear correlation between predicted and measured ponded depth values (R2 = 0.8379). The soil field-saturated hydraulic conductivity calculated by these two tests was approximately equal. These results demonstrate that the steady-state principle of CHWP could be used to select appropriate irrigation systems for ISDI, and ISDI provides another technique to obtain the field-saturated hydraulic conductivity. Key words: Constant-head well permeameter, field-saturated hydraulic conductivity, indirect subsurface drip irrigation, steady-state
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".