Clustering and Characterization of Toronto Soils Using Pressuremeter Tests
Bibliographic record
Abstract
This research aims to cluster and characterize Toronto soils based on pressuremeter tests (PMT) by applying machine learning algorithms. More than 400 PMT results were collected from a transit project in Toronto, Canada. Gaussian Mixture clustering was applied to cluster Toronto soils into four main groups with distinct mechanical behaviour: glacial tills I and II, cohesive, and cohesionless deposits. A material index based on the shape of the PMT curve was proposed to enhance the separation between cohesive and cohesionless soils, and PMT soil behaviour charts of material index (M IDvs. Menard modulus (E ) Mere proposed. The isocurves of the coefficient α can be used to estimate soil's elastic moduli by correcting the pre-boring PMT disturbance. Predictive equations for EMandM weID proposed enabling the application of the PMT charts in practice. The application of French PMT charts was investigated, and comparisons between Toronto and UK tills were performed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".