واسنجی روش گلف با روش پمپاژ به داخل چاهک کم عمق(SWPT) برای اندازهگیری ضریب هدایت هیدرولیکی واستخراج معادله تک عمقی لاپلاس و ریچاردز برای یک خاک لوم
Bibliographic record
Abstract
Saturated hydraulic conductivity is a vital soil propriety in controlling infiltration and runoff, drainage, extracting pesticides, and herbicides from soil profile and transfer them to ground water. The auger-hole method is the most famous and the most common method to measure the hydraulic conductivity (K) that have been used normally for years. Using this method is possible where the water table is high and in a one-meter range from the soil surface. In the measurement of saturated hydraulic conductivity some problems occur when the water table of the soil is very deep. In arid and semi-arid areas especially in summer, the water table is so low making it impossible to use ideal methods.To determine the hydraulic conductivity rates of soils above the water table, different methods are used. These methods have always been faced with weakness in theoretical bases or practical problems as well as being time consuming and costly. One of these methods is the shallow well pump-in test which is the most adaptable method used for this purpose. However, a new method has been developed to measure the hydraulic conductivity above water table which is called the Guelph Permeameter method. As the Guelph method was introduced by Reynolds and Elricks (1985), great changes have been made in this field, and due to the strong theoretical bases, being less time-consuming and cheaper to perform, Guelph method attracted lots of attention. The aim of this research was to calibrate the Guelph Permeameter for the measurement of saturated hydraulic conductivity using the Shallow Well Pump-in Test (SWPT) method at an experimental farm in Shahid Chamran University of Ahvaz. This research examines the calibration of Guelph Permeameter method by using shallow depth pumping test method for a loam soil in this region.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.034 |
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".