Metal emissions from a Cu smelter, Rouyn-Noranda, Quebec: characterization of particles sampled in air and snow
Bibliographic record
Abstract
Particles were sampled in air and snow near a Cu smelter in Rouyn-Noranda, Québec, as part of a study of airborne metal emissions. An analytical scanning electron microscope (SEM) was used to measure the size and elemental composition of >38 000 individual particles. Metal-bearing (Me-) particles account for c. 58% of all particles in the smelter plume, but only c. 15% in ambient air or snow. The dominant Me-particle type in snow is Fe–S–Cu but Zn–S, Fe–S, and Cu–S are also common. Pb is dominant in air-filtered particles, even those collected far (>60 km) from the smelter. Me-particles in snow are compositionally more variable and complex than in the smelter plume or ambient air, suggesting that Me-particles settling from the plume in snow are chemically transformed in the process, possibly by heterogeneous reaction(s) with other aerosols (e.g. salt particles) and/or gases (e.g. SO 2 ). The size distribution of Me-particles in the smelter plume is broader than in snow or ambient air, owing to a larger proportion of sub-micrometre particles in the plume and/or the loss of fine water-soluble Me-particles in snow meltwater. However, the size distribution of different Me-particle groupings (e.g. As-bearing compared to Cd-bearing particles) is not significantly different within the size range measured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".