Application of the atmospheric Lagrangian particle dispersion model MLDP0 to the 2008 eruptions of Okmok and Kasatochi volcanoes
Bibliographic record
Abstract
The atmospheric transport and dispersion model Modèle Lagrangien de Dispersion de Particules d'ordre zéro (MLDP0) has been in use at the Canadian Meteorological Centre (CMC) for several years. The model is employed to support environmental emergency response activities, in the context of CMC's national and international mandates. MLDP0 is a Lagrangian model in which diffusion is modeled according to a random displacement equation (RDE). MLDP0 is an off‐line model and is driven with meteorological fields from CMC's Numerical Weather Analysis and Prediction (NWP) system. MLDP0 can be executed in forward and inverse modes. During the summer of 2008, the important eruptions at Okmok and Kasatochi, in the Aleutians, were cause of considerable concern to aviation, and the model was used extensively to support the Montreal Volcanic Ash Advisory Centre (VAAC). Qualitative comparisons of satellite imagery and MLDP0 outputs show that the model accurately simulated the behavior of volcanic plumes. Inverse simulations based on SO2 observations of the Okmok plume, at the Washington State University campus in Pullman, Washington, yield emission estimates that agree well with those derived from AURA/OMI. Forward simulations using AURA/OMI SO2 emission estimates for the Kasatochi eruption of 7 August also compare quite well quantitatively with observations from Environment Canada's Brewer spectrophotometers in Toronto, as well as with concentration maps reconstructed from AURA/OMI scans.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".