Analysis of High Pressure Zone Attributes From Tactile Pressure Sensor Field Data
Bibliographic record
Abstract
Tactile sensor data collected during the Japan Ocean Industries Association (JOIA) medium-scale field indentation test program provide detailed information about spatial and temporal distributions of contact pressures during ice crushing. The localization of contact into high pressure zones (hpzs) through which the majority of loads are transmitted to the structure is an important feature of these data. For all but the slowest interaction rates, non-simultaneous failure is observed, with linear distributions of hpzs comprising a total contact area on the order of 10% of the nominal interaction area (structure width × ice thickness). To improve understanding of the nature of individual hpzs during compressive ice failure, a new approach to analyzing tactile sensor data has been developed. Analysis algorithms developed for automatic hpz detection and tracking are discussed. Issues associated with pressure threshold value definition and selection are considered. Probabilistic descriptions of high pressure zone attributes based on analysis of JOIA field measurements are presented. The development of a probabilistic ice load model based on these hpz data is detailed in a companion paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".