Optimizing the Use of Aircraft Deicing and Anti-Icing Fluids
Bibliographic record
Abstract
This report provides practical technical guidance on procedures and technologies to reduce the use of aircraft deicing and anti-icing fluids (ADAF) while maintaining safe aircraft operations across the wide range of winter weather conditions found in the United States and Canada. This guidance is presented as (1) a series of best management practices that are immediately implementable and (2) the detailed findings and recommendations of experiments to evaluate holdover time determination systems, spot deicing for aircraft frost removal, and ADAF dilutions. The report will be of direct interest to airport and airline staff responsible for aircraft deicing and anti-icing operations and the mitigation of their environmental impacts. Included with this report is a packet of 16 Fact Sheets describing promising technologies and procedures from Chapter 2, singly or in combination, in the form of readily implementable best management practices. Each Fact Sheet includes (1) a description of the technology or procedure; (2) implementation considerations; and (3) cost information. In 2016, the 16 Fact Sheets were reviewed to assess if they reflected current technologies and practices in the industry. That review resulted in updates to Fact Sheets 45, 55, and 56, and the creation of a new Fact Sheet 112. describing promising technologies and procedures from Chapter 2, in the form of readily implementable best management practices.\n
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".