Ice Loads in Dry and Submerged Conditions
Bibliographic record
Abstract
This paper describes a study of the effects of submergence on ice crushing loads. Thirty three small scale indentation tests have been performed on cone-shaped ice samples in dry and submerged conditions using a material testing system (MTS machine) located in a cold room at −7°C. The indenter was a flat aluminum plate at the bottom of a container that was attached to the actuator of the MTS machine. Transparent windows facilitated visual observations and recordings using a high speed camera. In the submerged tests the container was partly filled with salt water. Testing was performed at rates of 1 mm/s and 100 mm/s. The specimens were ice cones with 25 cm in diameter and with 20° and 30° angles. Data recordings comprised time-penetration and time-force histories. Generally higher forces were obtained in submerged tests. Furthermore, the difference between dry and submerged condition was more pronounced at the high indentation rate.
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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) | 0.003 | 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".