Bibliographic record
Abstract
Experiments using a mock ice-skating blade have shown that when the blade is sliding laterally on an ice surface at 30 mm/s, similar to a skater applying a pushing stride to accelerate or when stopping, crushing produces regular spallation events at the intactice/blade interface (where pressure is ~50 MPa) that result in a cyclic load pattern (spalling frequency ~ 85 Hz). During forward gliding there was also evidence of spallation events at the gently-upturned ‘bow’ of the blade (spalling frequency ~ 39 Hz). From the speed and force data, and the depths of the lateral shaving and the forward gliding track profile, the energy consumed per unit volume of ice removed was determined in both cases. The energy budget implied that 12% of the shaved ice material was liquid melt, and 18% of the thin sheet-like slurry of liquid and ice particles (< 0.1 mm thickness) extruding from the edges of the blade-bow during gliding was melt. More energy per unit volume was consumed in the gliding case because all spallation debris at the bow was removed from the track by highpressure compaction imposed by the over-running blade. This caused further pulverization and inter-particle ice-on-ice crushing generating mobile slurry that augmented slurry produced on intact-ice hard-zone areas at the blade-ice interface. In the lateral shaving case less material was converted to slurry because many unconfined pieces of the shattered spalls were not subjected to further pulverization/extrusion. This was evident from the post-test observation of the accumulated shaved material that showed spall debris mixed with refrozen extruded slurry. The crushing of ice and presence of slurry under a skate blade are important observations because earlier ice crushing-friction experiments have shown that a thin (< 0.2 mm) highly-lubricating slurry (~16 % liquid) is produced at high-pressure hard-zone contact regions, which facilitates extremely low friction.
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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 | 0.001 |
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".