Contribution to IPW1 by Studying Multi-Layer Icing Convergence in 2D/2.5D
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3308.vid A subset of the cases presented at the 1st AIAA Ice Prediction Workshop by Polytechnique Montreal are shown in this paper. The simulation results are produced using two ice accretion software developed at Polytechnique Montreal, namely NSCODE-ICE and CHAMPS. Both can simulate the ice accretion using a multi-layer approach in 2D/2.5D, but only CHAMPS can simulate in 3D although in single layer. The presented cases were chosen to focus on the impact of the number of ice layer, which relates to the temporal accuracy of the method, the turbulence modeling within the flow solver and the stochastic simulation of ice accretion. The results are deemed satisfactory with regards to the experimental variability on the chosen test cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".