Data-driven recovery of incomplete geotechnical dataset using low-rank matrix completion
Bibliographic record
Abstract
Real geotechnical data from a typical site might be characterized as MUSIC-X (i.e., multivariate, uncertain, unique, sparse, incomplete, and potentially corrupted, with X denoting spatial/temporal variability). One of the key challenges in developing site-specific statistical models for multiple geotechnical properties (i.e., multivariate) is missing (or incomplete) values from different tests at various depths/locations. This raises a critical question in geotechnical site investigations: how to recover the missing values in real geotechnical datasets from available measurements by leveraging the underlying structure of geotechnical datasets? Since different geotechnical properties are not only correlated among different properties, but also auto-correlated across different depths, this suggests that a simple underlying structure with only a limited number of important features/patterns might exist for multivariate geotechnical datasets. Leveraging on this observation, this study proposes a novel, data-driven method for predicting missing values by low-rank matrix completion. The proposed method exploits the auto- and cross-correlation structures of different test data. Missing values are then recovered using a singular value thresholding algorithm, and a k-fold cross-validation strategy is employed to determine the level of measurement noise. The method is illustrated and validated using a real geotechnical dataset. The results indicate that the proposed method can provide reliable predictions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".