Characteristics of Field-scale High Pressure Zones during Non-simultaneous Failure of Thin First-year Sea Ice
Bibliographic record
Abstract
For temperate ice regions, the guidance provided by current design codes regarding ice load estimation for thin ice is unclear, particularly for local pressure estimation. During the non-simultaneous failure of ice under compression, spalling fracture localizes contact into high pressure zones (hpzs), through which the majority of loads are transmitted. Much of our present understanding of hpzs comes from inferences made from the analysis of pressure panel data collected during medium-scale field tests or full-scale measurements on ships or structures. During medium-scale field indentation tests conducted by the Japan Ocean Industries Association (JOIA) from 1996-2000, tactile pressure sensors were also deployed. The JOIA dataset provide detailed information about pressure distributions at a sufficiently high resolution so as to allow for the identification and tracking of individual hpzs throughout an interaction. Given their importance in the transmission of loads during an ice-structure interaction, understanding the birth, evolution and death of individual hpzs is seen as being an important direction both for guiding fundamental studies of ice mechanics and also for guiding the development of new ice load models. Recent analysis of these tactile sensor data has led to the development of an empirical hpz-based model which can be applied to model local and global pressures for thin ice conditions (Taylor and Richard, 2014). From this analysis, new insights into the nature of hpzs for thin first year sea ice during non-simultaneous failure have resulted. In the present paper, an overview is provided of analysis techniques used to extract information about individual hpzs from the tactile sensor dataset, as well as the characteristics of these hpzs. Aspects discussed include spatial and temporal characteristics of high pressure zones, as well as pressure and geometric attributes. While observations of the shape of spatial distributions and total contact area covered by hpzs are consistent with previous observations (line-type distributions with total contact area on the order of 10% of the nominal interaction area), these results indicate that individual hpzs are smaller and more densely distributed than indicated by previous analyses based solely on pressure panel data. The implications of this finding in terms of scale effects and ice load modeling are discussed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".