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Record W2080379090 · doi:10.1061/40646(2003)25

Environmental Impact of Aircraft Deicing

2002· article· en· W2080379090 on OpenAlexaboutno aff
Derek R. Gray

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsIcingRetrofittingEnvironmental scienceStormwaterWaste managementEnvironmental engineeringEngineeringSurface runoffMeteorology

Abstract

fetched live from OpenAlex

Ethylene glycol based fluids are the primary aircraft anti-icing/deicing fluids used at Toronto Pearson International Airport. Due to the toxicity of the additives to anti-icing/deicing fluids and the oxygen limiting ability of ethylene glycol, significant efforts are required to control the release of stormwater contaminated with aircraft anti-icing/deicing fluids. It was recognized that both source control and end of pipe measures are required. Source control measures were implemented via retrofitting existing facilities and the construction of the Central Deicing Facility. Since 1990, a Master Stormwater Implementation Plan has been developed to design and construct appropriate end of pipe facilities at Toronto Pearson International Airport, at a cost of over $100 million. The combination of both source control and end of pipe measures have resulted in fewer "exceedances" of the Canadian Environmental Protection Act guideline for glycol contaminated runoff.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0650.002

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.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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