Diatoms as indicators of long-term nutrient enrichment in metal-contaminated urban lakes from Sudbury, Ontario
Bibliographic record
Abstract
The majority of the limnological research in Sudbury, Ontario, has focused on the anthropogenic impacts of industrial emissions (SO2 and metals), with the potential effects of cultural eutrophication largely being overlooked. However, the population of the City of Sudbury has grown with the prosperity of the mining sector, which poses a risk to the quality of freshwater resources. As with many environmental issues, there is often a lack of predisturbance data that can assist in gauging the full extent of environmental change. Therefore, paleolimnological approaches were used to track long-term biological changes in sedimentary diatom assemblages related to cultural eutrophication in 4 lakes from Sudbury. Diatom assemblages were primarily dominated by oligotrophic taxa prior to watershed development; however, with the onset of urban environmental stressors (e.g., septic systems, the application of lawn fertilizers and watershed development), there was a shift toward taxa that thrive in more productive systems. Diatom assemblages also seem to track an increase in lakewater pH through time, which is likely related to increased acid neutralizing capacity as a result of watershed disturbances, algal assimilation and bacterial reduction of NO− 3, and increased base cation export from the watershed due to acidic deposition. Insight into predisturbance conditions of the lakes should help lake managers set realistic biological targets for restoration and may be used to help gauge the response of these systems to future mitigation efforts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".