Round-Robin Study for Ice Adhesion Tests
Bibliographic record
Abstract
Ice adhesion tests are widely used to assess the performance of potential icephobic surfaces and coatings. A great variety of test designs have been developed and used over the past decades due to the lack of formal standards for these types of tests. In many cases, the aim of the research was not only to determine ice adhesion values, but also to understand the key surface properties correlated to low ice adhesion surfaces. Data from different measurement techniques had low correspondence between the results: Values varied by orders of magnitude and showed different relative relationships to one another. This study sought to provide a broad comparison of ice adhesion testing approaches by conducting different ice adhesion tests with identical test surfaces. A total of 15 test facilities participated in this round-robin study, and the results of 13 partners are summarized in this paper. For the test series, ice types (impact and static) as well as test parameters were harmonized to minimize the deviations between the test setups. Our findings are presented in this paper, and the ice- and test-specific results are discussed. This study can improve our understanding of test results and support the standardization process for ice adhesion strength measurements.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".