New NRC Snow Test Environment Part 1 System Capabilities and Characteristics
Bibliographic record
Abstract
The goal of this work was to develop and characterize a new artificial snow making system at NRC to produce a simulated falling snow environment meeting the specifications setup by the industry members of the Ice Genesis project. This system was first tested in the summer of 2021 at the Gas Turbine Lab (GTL) Research Altitude Test Facility (RATFac) and then upgraded for a second test campaign from July to September 2022. Snow-like particles are created by agglomerating ice particles from the NRC ice crystal icing (ICI) system, and then injected into the cascade rig icing tunnel. This type of particle is consistent with observations in nature where the most prevalent category of falling snow particle is aggregates. A range of system operating parameters were evaluated to examine the range of test conditions that could be achieved and were in good agreement with that observed in nature. This paper focuses on characterizing the artificial snow environment and Part 2 examines the accretion characteristics in this environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.006 | 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; both teacher heads agree on what is shown here.
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".