The Canadian Meteorological Centre's Atmospheric Transport and Dispersion Modelling Suite
Bibliographic record
Abstract
This paper describes the integrated suite of Lagrangian transport and dispersion models in operation at the Canadian Meteorological Centre. These models have been in use for several years and are applied to many types of environmental emergencies covering spatial scales from the very local to the global. The Modèle Lagrangien Courte Distance (MLCD) is used for atmospheric spills of the order of a few kilometres. The Modèle Lagrangien de dispersion de particules d'ordre 1 (MLDP1) is normally used for events affecting areas less than 100 km; Modèle Lagrangien dispersion de particules d'ordre zéro (MLDP0) is used for events of continental and global consequences. The Modèle Lagrangien dispersion de particules mode mixte (MLDPmm) alternates between first-order and zeroth-order depending on criteria specified by the user. The theoretical bases of the models are presented, and the main algorithms used in their implementation are discussed. Modelling of the diffusion processes is based on a stochastic differential equation with the assumption of quasi-stationary Gaussian turbulence, locally homogeneous in the horizontal. The practical aspects of the operational implementation are also described. Using these models, results from simulations of real cases on scales ranging from the very local, to a few kilometres, to regional (approximately 100 km) to continental (approximately 1000 km) and to global (approximately 10,000 km) are compared and validated with available observational data.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".