Bibliographic record
Abstract
Contrails and contrail cirrus are the largest contributors of aviation radiative forcing, yet the exact quantification of their global impact remains far more uncertain compared to other sources, such as direct CO2 emissions. This uncertainty involves all phases of contrail lifetime –from formation until dilution in the free atmosphere. It is known for example that aircraft induce persistent contrails when flying in ice-supersaturated regions by providing condensation nuclei (soot particles or liquid aerosols) onto which ice nucleates and accumulates. These processes are strongly non-linear and also depend on the atmospheric conditions and engine setup among other parameters. Since it is not possible to explore the effects of all these parameters using detailed modeling such as 3D large-eddy simulations, low or mid-fidelity modeling approaches have been used in the literature with mixed success. In an effort to assist industry and modelers with design tools and flight trajectories definition, we developed an efficient computational method based on Reynolds Average Navier Stokes (RANS) simulations coupled to a stochastic model that captures the essence of jet turbulent mixing and the microphysical processes occurring in the plume. The method is validated for pure mixing using existing database of jet flow experiments and simulations, and it is then applied to contrail formation by activating simple ice microphysical models.
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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 | 0.000 |
| 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".