Numerical simulation of air concentration and deposition of particulate metals around a copper smelter in northern Quebec, Canada
Bibliographic record
Abstract
A three-dimensional numerical modelling study of the dispersion of particles emitted from a copper smelter at Rouyn-Noranda, Quebec, was conducted using the BLFMAPS, a mesoscale boundary layer forecast and air pollution prediction system. This numerical modelling system was used to simulate meteorology and air concentration, dry deposition and wet deposition of particulate matter emitted during February and July 2000. During these time periods an instrumented research aircraft measured the chemical and physical properties of the particles in the plume. Simulations were done for particulates with three aerodynamic particle diameters of fine (0.25 μm), medium (4 μm), and large (20 μm). The comparison of model-predicted air concentration for a few metals with the aircraft-measured data provided reasonable agreement. Particle-size-dependent deposition showed some interesting patterns and phenomena. Coarser particles have a stronger deposition rate than finer particles. Finer particles have a longer lifetime in the atmosphere and transport over long distances. Specifically, c . 95% and 85% of fine particle emissions during winter and summer study periods, respectively, were exported >100 km from the smelter plant, whereas only c . 50% of large particle emissions were exported >100 km from the plant during both study periods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".