Spatial prediction of rockhead profile using the Gaussian process regression method
Bibliographic record
Abstract
The spatial distribution of rockhead profile has a significant impact on the design and construction of geotechnical engineering structures. Limited by the economic and technical conditions, the borehole data of specific sites are often sparse, which brings great challenges to the accurate prediction of rockhead profile. In this study, a Gaussian process regression (GPR) method is used to predict the rockhead profile. Borehole data from a construction site in Hong Kong are used to evaluate the ability of the GPR method to predict rockhead profile. Besides, the influences of the amount of borehole data on the accuracy of the GPR model and the spatial prediction results of rockhead are investigated. The results indicate that the GPR model based on the square exponential and the Matérn covariance function can obtain more accurate prediction results, and the GPR model with limited borehole data can provide a reasonable prediction interval for rockhead depth. With the increase of borehole data, the generalization ability and prediction accuracy of the GPR model gradually improves. In engineering practice, the prediction accuracy and uncertainty degree of the GPR model can be used to judge whether it is necessary to continue to increase borehole data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".