INTRODUCING THE SHEAR-CAP MATERIAL CRITERION TO AN ICE RUBBLE LOAD MODEL
Bibliographic record
Abstract
Current ice rubble load models are based on a cohesive-frictional stress criterion, of which the Mohr-Coulomb (MC) and Drucker-Prager (DP) models are the most widely used. Combining these failure criteria with a shear-cap model as a new material model provides limits to the behaviour of rubble in both deviatoric and hydrostatic stress states. This concept adds a cap yield surface to the DP shear yield criterion bounding the yield surface in hydrostatic compression. The cap yield surface controls the dilatation process by including a volumetric hardening parameter. A detailed parametric analysis has been done to investigate the range of the shear-cap model's parameter values that provide valid results. These parameters have been calibrated to implement the shear-cap material criterion into an analytical load model. This analytical load model calculates the load of interaction between first year ice ridge keels and conical structures. Existing models significantly over-predict the recorded loads measured at the Confederation Bridge Pier (P31). As a result, one of the aims of the newly developed load model is to give calculated load values which correlate well with the Confederation Bridge recorded loads. Comparison has been made between the calculated pressures using the new model and the recorded pressure given by the Confederation Bridge Monitoring Program (Brown, 2006). The comparison showed promising correlation between the calculated and recorded pressures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".