Effects of Copper, Nickel, and Sulfate from the Smelters at Sudbury, Ontario (Canada) on Microbial Communities in Lakes
Bibliographic record
Abstract
Analysis of water and sediments from deep and shallow environments in lakes located 6–154 km east or southeast of the smelters at Sudbury, Ontario (Canada) revealed variable, interactive effects of copper, nickel, and sulfate from smelter fallout on lacustrine microfloras. Metal species in sediments were differentiated by sequential extractions, and the nature, abundances, and activities of microbial populations were represented by chlorophyll-a in water and by CO2 production, fatty acids, phospholipids, dehydrogenase, alkaline phosphatase, and spectral properties of humic matter in sediments. Smelter fallout declined logarithmically with distance from the smelters, and its effects on microfloras depended on the type of microorganism or microbial process and on environmental factors and the abundances of metal species and detoxifying agents. Extractable copper and nickel had toxic effects, which were not attributable solely to the exchangeable fractions, but in certain cases nickel counteracted copper toxicity. Sedimentary sulfide as a whole or sulfide produced by bacterial sulfate reduction, or low oxidation–reduction potential regardless of sulfide concentration, ameliorated metal toxicity by making the metals less bioavailable; and toxicity showed a “quantum jump” when detoxifying agents fell below certain critical concentrations, implying the existence of threshold levels of bioavailable metals above which toxicity increased abruptly. In some cases metal toxicity was lowest in the lakes closest to the smelters (because sulfate concentrations were highest) as well as in the lakes furthest away, and was highest at intermediate distances. The results also suggest that nickel pollution led to ecological succession whereby nickel-tolerant microbial populations replaced nickel-sensitive ones.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".