SLD Instrumentation in Icing Wind Tunnels – Investigation Overview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2647.vid A collaborative effort to better understand cloud characterization probes in Supercooled Large Drop (SLD) conditions, as well the ability to simulate these conditions in several icing wind tunnels, was undertaken by NASA, NRCC, CIRA, ECCC, FAA and Met Analytics, Inc. Both drop sizing and liquid water content, LWC, were measured with various probes using current to emerging technologies. To ensure the best possible data quality from the newest probes, the probe manufacturers, SEA, Inc. and Artium, Inc. were invited to support testing and data analysis efforts. A common set of probes was identified to test in each of the three participating facilities: NRCC’s Altitude Icing Wind Tunnel, NASA’s Icing Research Tunnel and CIRA’s Icing Wind Tunnel. From the common set of probes, a subset were identified to use for comparison across the three facilities. These were the CDP-2 and 2D-S for drop sizing, and the Multi-wire for LWC. The LWC value was also checked by measuring the ice accretion thickness under hard rime conditions on a NACA-0012 airfoil. A common test matrix with sweeps in both LWC and median volume diameter, MVD, was developed. Each facility achieved these conditions as determined by their own calibration. The MVD ranged from 20 to at least 200 um, and LWC ranged from 0.5 to 3 g/m3. The comparison probes tested at common conditions in each facility were intended to allow for a direct comparison, and check of potential facility bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 it