Kriging Surface Interpolation and Its Application Based on Self-adaptive Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Semi-variant function as an important mathematical model of Kriging spatial analysis can effectively describe the features of the variants in some districts of ore deposit. Semi-variant function parameter estimation affects the Kriging surface interpolation precision directly. Firstly,this paper adjusts the mutation probability of genetic algorithm to avoid premature convergence and to guarantee the algorithm efficiency. Secondly,the paper improves the Kriging semi-variant function for surface interpolation using the self-adaptive genetic algorithm. At last,the improved Kriging is applied in creating hydrocarbon source rock surface of reservoir simulation. The simulation effect through Kriging is compared to the effect of inverse distance weighted method. The comparative result shows that the surface interpolated by improved Kriging fits better with the practical discrete points in the practical engineering application. The improved Kriging embodies the effect of engineering exploration data sufficiently and is more suitable for engineering requirement.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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