Surface Temperature Fluctuations in Ice Indentation Tests
Bibliographic record
Abstract
Ice-structure interactions produce numerous, short-lived high pressure zones which transfer the majority of the load to the structure. Ice indentation tests have been used to observe the fluctuations in surface temperature at these zones. Previous investigations have shown that temperature fluctuates inversely to the applied load during cyclic loading. These fluctuations are believed to be due to rapid melting and refreezing cycles in the high pressure zones. A recent series of small-scale indentation tests investigated surface temperature fluctuations for indentor speeds at three orders of magnitude (0.21 mm/s to 21 mm/s). Freshwater granular ice specimens were grown in cylindrical steel moulds using seed ice and distilled water. A 70 mm diameter indentor with a radius of curvature of 89.6 mm was indented into the ice to depths between 10 and 15 mm. Seven thermocouples were installed in the indentor and made flush with its surface. These were used to measure the surface temperature of the ice during indentation. All tests took place at −10 degrees. Notable changes in temperature response were observed with changing speed. A steady increase in temperature was observed during low speed tests. Medium speeds lead to fluctuations in temperature due to the presence of spalling and cyclic loading that is consistent with the results of others. Some evidence of the inverse relationship between load and temperature can be observed in the highest speed tests, but the thermocouples sampling frequency was too low to accurately respond to high frequency loading and could not provide clear results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".