Delineating Areas of Past Environmental Degradation near Smelters using Rock Coatings: A Case Study at Rouyn-Noranda, Quebec
Bibliographic record
Abstract
Abstract Emissions of SO2 from smelters can promote formation of acid rain, which can dissolve siliceous minerals on exposed rock surfaces and promote the formation of silica gel layers within which detrital and smelter-derived particulates can become trapped. These processes of dissolution and entrapment can result in the formation of rock coatings that contain elevated levels of heavy metals. Between 1927 and 1976, the Horne smelter processed sulfide ore derived from the Rouyn-Noranda region and became one of the largest emitters of particulates and sulfur dioxide in North America, promoting the formation of coatings on nearby rock surfaces. The reflectance spectra of these coatings are relatively flat, with typical reflectance values ranging between ~5% at visible wavelengths and ~16% in the shortwave infrared. Absorption troughs in coating spectra are consistent with the presence of materials including opaline silica, olivine, pyroxene, hydrous phyllosilicates, and sulfates. Classification of Landsat 8 Operational Land Imager data indicates that rock coatings near Rouyn-Noranda comprise a total surface area of ~1.5 km2, nearly all of which is located within ~6 km of the Horne smelter. Remote sensing techniques can used to delineate the geographic extents of coatings near smelters, highlighting areas previously subjected to severe environmental degradation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".