Optimizing the Use of Aircraft Deicing and Anti-Icing Fluids
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
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
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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