High-speed imaging of mechanisms responsible for sawtooth cyclic loading during ice-crushing
Bibliographic record
Abstract
High-speed imaging (30,000 images/s) has been used to observe the behavior of monocrystalline freshwater ice during crushing at -10°C. The ice contact zone consisted of an intact high-pressure central area surrounded by pulverized spall debris and partially refrozen melt. A sawtooth pattern was evident in the load record, similar to that observed in other ice crushing and indentation studies. The spalling behavior that is responsible for any particular load drop in the load record was found to be caused by the presence of small stable fractures in the high-pressure intact ice zone that were created during the preceding spall event and associated load drop. The small fractures are roughly centered in the intact ice region and this explains why the magnitude of the load drops is typically half the prior load value. The removal of ice from the intact ice zone by the viscous radial flow of a thin layer of melt has been described before (Gagnon, R.E. 1994. Melt Layer Thickness Measurements During Crushing Experiments on Freshwater Ice. Journal of Glaciology, 1994, Vol. 40, No. 134, 119-124). This process explains why the small fractures are roughly centered in the high-pressure contact zone. There the flow is stagnant and less ice has melted, relative to the non-central areas, and this leads to high non-uniform stresses in the ice that cause the fractures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".