Methodology for Combined Local and Global Ice Pressure Estimation based on a Probabilistic Model of High Pressure Zone Behavior Derived from Field Data
Bibliographic record
Abstract
Abstract As our understanding of the failure processes and mechanics associated with individual hpz behavior advances, new models are needed to link hpz behavior with local and global pressures for full-scale interactions. This paper is focused on the development of a probabilistic compressive ice load model for thin first-year sea ice based on statistical descriptions of high pressure zones (hpzs) derived from the analysis of tactile sensor data collected during the Japan Ocean Industries Association (JOIA) medium-scale field indentation test program. The aim of this work is to provide a probabilistic model of hpz behavior that simulates observed local and global pressures and associated scale effects, particularly for interactions involving thin first-year sea ice. Probabilistic descriptions of high pressure zones based on field measurements have been extracted and are incorporated into a model for linking hpzs with methodology for ice pressure estimation. A comparison with local and global pressure design curves generated using methodology provided by ISO 19906 is included.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".