Comparison of Human Ice Detection Capabilities and Ground Ice Detection System Performance under Post Deicing Conditions
Bibliographic record
Abstract
Currently, after deicing operations, the presence of residual ice on an aircraft's wing is determined by a human deicer from a deicing ground crew via visual and tactile inspections. One method proposed to overcome some of the safety and physical concerns associated with human inspections is to use Ground Ice Detection Systems (GIDS). However, before regulatory authorities can consider GIDS for operational use, their performance had to be compared to human ice detection capabilities. In August 2005, the Federal Aviation Administration (FAA) William J. Hughes Technical Center's (WJHTC) Simulation and Analysis Group conducted a study sponsored by the FAA Office of Aviation Research, Flight Safety Branch (WJHTC), and Transport Canada's Transportation Development Centre to compare human ice detection performance using current visual and tactile techniques with the performance of two different GIDS under post deicing inspection scenarios. Nine male deicers from Globe Ground at Toronto Pearson Airport and Aero Mag 2000 Montreal performed post deicing inspections using three methods: the current method (Visual inspections and Tactile inspections), the GIDS1 method, and the GIDS2 method. Three separate post-deicing scenarios were presented each day for three days: a wing with 12 ice patches (High Contamination), three ice patches (Low Contamination), and a clean wing (No Contamination). Accuracy data, false detection data, and time to complete an inspection were collected and analyzed for each condition. The results from the study consistently indicated that overall GIDS1 was superior to human visual and tactile inspections and GIDS2 inspections in terms of accuracy, false detections, and stability in performance. Participants using GIDS1 were able to detect all patch sizes and thicknesses with the greatest accuracy while the other methods' accuracy improved as a function of patch size and thickness. In addition, inspections completed by the GIDS1 manufacturer throughout the study suggest that, with time and experience, performance could further improve.
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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 | 0.001 |
| 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".