Apparatus design evolution and supporting experiments for a novel technique to study ice crushing
Bibliographic record
Abstract
Ice crushing experiments similar to Gagnon and Daley (2005) have been repeated at twice the former high-speed video rate (1000 images/s) and with the intention of eliminating in-plane fractures that occurred in all previous tests. Rectangular thick sections (1 cm thickness) of lab-grown monocrystalline ice were confined between two thick Plexiglas plates and crushed at -10°C from one edge face at a rate of 1 cm/s using a transparent Plexiglas platen (1 cm thickness) inserted between the plates. Visual data were recorded from the side using the high-speed video and vertically through the platen using regular video. It was concluded that the system was too compliant to prevent the in-plane fractures, however, one test did show near full-thickness intact ice. From this test unambiguous pressure measurements were obtained for the intact and pulverized ice at the ice/platen interface utilizing the system's novel pressure sensor. As in the previous report, the production and flow of liquid in a thin layer at the intact ice/platen interface was evident. The apparatus was then modified significantly to reduce its compliance and more tests were conducted with the eventual desired result that in-plane fractures in the ice were eliminated. This confirmed that the apparatus is capable of yielding visual data of a 2-D slice of ice during crushing as though it was part of a larger piece of ice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".