Mechanics of Ice Rubble Over Multiple Scales
Bibliographic record
Abstract
The mechanics of ice rubble plays an important role in many different engineering applications, including ice-structure interactions with oil and gas infrastructure, river and lake engineering, and ship-ice interactions in northern shipping lanes. Of particular interest are the massive accumulations of rubble formed by shear or compression in the ice cover, which consolidate to form sea ice ridges that can be hazards to such structures. These are common ice features in Arctic and sub-Arctic environments and as a result often govern the design loads for ships, coastal and offshore structures operating in these environments. In addition, ridge keels can scour the seafloor in relatively shallow waters posing a threat to pipelines and other subsea facilities. It is not clear what load an ice rubble feature can exert on a given structure and how it will deform. It will depend on a number of parameters including the age of the feature, its composition and structure, and its strength and failure behaviour. This paper will examine the mechanical properties of ice rubble over multiple scales. The paper will begin at the one block level, describing how ice block properties vary over time, before advancing to look at the bonding/sintering processes that occur between two ice blocks and eventually the processes that take place between multiple ice blocks (i.e., ice rubble) and large scale sea ice ridges. Particular attention will be paid to the effects temperature and pressure have on ice rubble, as these parameters are believed to be important to our understanding of its behavior.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".