A Geo-statistical Approach to the Change Procedure Study of Under-Ground Water Table in a GIS Framework, Case Study: Razan-Ghahavand Plain, Hamedan Province, Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Qualitative and quantitative parameters of underground water resources could be studied by taking discrete samples at different locations in study area. In this way, for change procedure study of mentioned parameters and to gain a deeper understanding of the phenomena, a continues surface need to be generated. The main purpose of Geo-Statistical analysis is creating or interpolating a continuous surface from discrete sample points; in the other hand, Geo-statistical Analyst, derives a surface using values from the measured sampling points to predict values for each location in the landscape. The ultimate goal is to produce a surface of predicted target values. In Geo-Statistics, there are two main categories for interpolation: deterministic and geostatistical. The first category, based on the similarity and distance from measured points (Inverse Distance Weighting, Global Polynomial Interpolation, and Local Polynomial Interpolation) or the degree of smoothing (Radial Basis Functions); and second category employs some statistical properties of observed samples (Kriging and Co-Kriging). This paper utilizes Geo-statistics for the estimation of underground water table at location of interest where measured values are not available. In this regard, by employing Kriging, IDW and RBF procedures, underground water table contours have been created. For best estimator selection, results have been validated by some statistical indices such as Mean Absolute Error (MAE), Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Biased Error (MBE) and Coefficient of Correlation. Absolute relative error variation curve for the number of sample points used for examination of efficiency and accuracy of each GeoStatistical estimators. Eventually, Kriging method has been selected as the best method to estimate the underground water table of Razan-Ghahavand plain. In order to Kriging estimations, direction of underground flow, which is the major relevant issue to Hydro-Geologic studies, has been gained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 it