Aviation Emissions Index Derivation Methodologies from Flight Data, including Black Carbon and Aerosols
Bibliographic record
Abstract
The NRC has undertaken aviation emissions flight research. Principally aimed at Heavy and Super Jet Transports, the project has applied the NRC T-33 to measure condensation nuclei (CN), black carbon (BC), volatile organic compounds (VOC) and oxides of nitrogen (NOy) from aircraft flying enroute at high altitude. Most recently, the T-33 and flight profiles, developed under the research, have been applied to the comparative measurement of jet biofuel emissions. During the course of the project, techniques have been developed, for the derivation of estimated Emission Indices (EI) of pollutant species, measured on the ground (during taxy, departure or arrival runway operations) or inflight. In particular, if the jet engine exhaust jet streams were very young, air temperature measurements were used to correlate to empirically modeled turbulent jet mixing fluid dynamics, thereby deriving the applicable dilution factors (DF) to apply to the measured concentrations. For exhaust plumes which were not very young (i.e. not in a state of turbulent mixing, but rather in a state of turbulent or quiescent diffusion), flight data has involved techniques for the full cross-sectional measurement of wakes, including jet wake and wake vortex regimes. Not only has this captured the nature of wake structures under very strong static and dynamic influences, but permitted the measurement of aerosol emission index numbers directly. © 2012 by The Crown in Right of Canada. Published by the American Institute of Aeronautics and Astronautics, Inc.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".