Effect of autocorrelation function model on spatial prediction of geological interfaces
Bibliographic record
Abstract
This study evaluated the performances of various autocorrelation function (ACF) models in predicting the geological interface using a well-known conditional random field method. Prediction accuracies and uncertainties were compared between a flexible Matérn model and two classical ACF models: the Gaussian model and the single exponential model. The rockhead data of Bukit Timah granite from boreholes at two sites in Singapore as well as simulated data were used for the comparisons. The results showed that the classical models produce a reasonable prediction uncertainty only when its smoothness coefficient is consistent with that of the geological data. Otherwise, the classical models may produce prediction errors much larger than that of the Matérn model. On the other hand, the prediction accuracy of the Matérn model is affected by the spacing of the boreholes. When the borehole spacing is relatively small (< 0.4 × scale of fluctuation), the Matérn model can reasonably quantify the prediction uncertainty. However, when the borehole spacing is large, the prediction by the Matérn model becomes less accurate as compared with the prediction using the classical models with the right value of smoothness coefficient due to the large estimation error of the smoothness coefficient.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".