Ice Detection Capabilities under Aircraft Post Deicing Conditions
Bibliographic record
Abstract
Human visual and tactile ice detection capabilities while inspecting deiced aircraft surfaces have not been quantified. Six male professional deicers from AeroMag 2000 Montreal participated in the experiment. We used a cold chamber to simulate one of the conditions experienced in the operational deicing environment. Ice samples were created by APS Aviation on white painted aluminum panels. Ice thicknesses ranged from 0.2 mm to 1.0 mm and were covered with aircraft deicing fluid. We used a two-alternative forced-choice procedure in which we showed a deicer a panel, then a second panel, and finally asked him to indicate on which of the two ice was present. Our data showed that, within the range of thicknesses we presented, deicers were unable to visually detect ice of any thickness on the white painted panels, but could easily detect ice using a tactile check.
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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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".