Regional and historical distributions of lake-water pH within a 100-km radius of the Horne smelter in Rouyn-Noranda, Quebec, Canada
Bibliographic record
Abstract
The spatial distribution of lake-water pH near the Horne smelter in Rouyn-Noranda, Quebec, Canada, is affected not only by industrial sulphur dioxide emissions, but also by other anthropogenic and natural factors. Regional calcareous glaciolacustrine deposits from glacial lakes Barlow and Ojibway provide buffering capacity. Locally, some kettle lakes are buffered by silicate weathering. Small mines, tailings, and natural sources of acidity (e.g. peat basins, forested catchment areas with acidic soils) also contribute to lake acidification. Interpreting the modern regional distribution of lake-water pH, ranging from 3.7 to 9.3 in 99 lakes, is equivocal in this area where wind-transported emissions and buffering from calcareous glaciolacustrine deposits can yield similar spatial trends. However, in a parallel study, diatom assemblages have been used as bio-indicators of past lake-water pH since they respond to temporal shifts in environmental conditions, including lake-water acidity. The historical pH reconstructions for two lakes within a 100-km radius of Rouyn-Noranda suggest that they were naturally acidic before industrialization and have further acidified. Lac de la Pépinière was naturally acidic in the 1800s (pH 5.5) and reached a pH of 4.8 by 1998; its increased rate of acidification since 1927 corresponds to the beginning of mining and smelting operations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".