Interpolation methods for spatial distribution of groundwater mapping electrical conductivity
Bibliographic record
Abstract
This study was carried out to develop a conceptual framework for determining the best interpolation method which mainly is employed to calculate the variability maps of electrical conductivity (EC) in neighboring regions. The considered case study is parts of the Khorasan Razavi province, Iran (including five aquifers Kashmar, Fariman, Doruneh, Sarakhs and Joveyn). In the first step, the empirical variogram (semi-variogram) was computed for the study area. The methods of the variability of a variable with spatial or temporal distance were considered to measure the semi-variogram function. In the next step, the best variogram model (e.g. spherical, exponential or Gaussian) was considered in the Geographic Information System (GIS) environment and f for the Environmental Sciences (GS+) software. By plotting the semi-variogram in GS + program based on different method as Global Polynomial Interpolation (GPI), Inverse distance weighing (IDW), Radial basis function (RBF), Kriging method, Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), the best variogram model fitted to spatial structure of the EC. Finally, by considering the acceptable range for different parameters which impact on EC and evaluating their impacts by scaling, the best interpolation method has been selected for that area for employing their neighborhood basin. Result indicated that the precipitation located within the range of 140 to 180 mm, RBI has the priority. This process is continued for all 14 parameters and eventually one method gets the most points.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".