Modeling the Impact of Airport Deicing/Anti-icing Activities on the Dissolved Oxygen Levels in the Receiving Waterways
Bibliographic record
Abstract
This paper evaluates the efficacy of the environmental management system (EMS) at Dallas Fort Worth International Airport (DFW) in capturing spent aircraft deicing fluids to prevent their discharge into receiving waterways.DFW claims that its EMS captures the spent aircraft deicing fluids completely, leaving only the spent aircraft anti-icing fluids to contribute to drip and shear during taxiing and takeoff and subsequent runoff to the waterways.Glycols in the aircraft deicing and anti-icing fluids reduce dissolved oxygen (DO) in the airport's receiving waterways upon mixing with it during aircraft deicing operations.To evaluate the airport's EMS claim, two decision tree models were built: one with anti-icing glycol usage as a predictor variable, and one with separated deicing glycol usage and anti-icing glycol usage as predictor variables.The analyses suggest that deicing glycol usage is more significant for predicting DO concentrations in the airport's receiving waters than is anti-icing glycol usage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".