Littoral diatoms as indicators of recent water and sediment contamination by metals in lakes
Bibliographic record
Abstract
We studied the response of benthic diatoms to recent metal contamination in littoral cores collected at 25 sites in 11 lakes situated at different distances from a smelter in the Rouyn-Noranda mining region (Quebec). Diatom response was described in terms of density, diversity, and taxonomic composition of the entire assemblages and as abundance of individual indicator taxa. Metal concentrations were measured in sediment and in the overlying water (as total dissolved and as free-ions). Sediment metal contamination was significantly higher in lakes located <10 km from the smelters than in lakes farther away. Such difference was not significant when metal concentrations in the overlying water were considered. Metal contamination did not affect diatom density, which indeed was highest in the most contaminated lake. Diversity (either measured as number of taxa or as Shannon and evenness indices) was instead significantly higher in lakes close to the smelter than elsewhere. Redundancy analysis indicated that diatom composition changed along a gradient in alkalinity (CO₃) and one in sediment metal contamination (Cd, Hg, Cu). We identified three diatom taxa (Fragilaria construens var. venter, F. construens var. pumila, and Brachysira vitrea) that increased in relative and absolute abundance with metal contamination. Benthic diatom responses at the community (density, diversity, assemblage composition) and population levels (abundance of selected benthic taxa) were stronger to the sediment metal contamination than to the contamination of overlying water. Comparisons with available literature indicated that, for monitoring recent sediment contamination, diatoms in littoral sediments are preferable to invertebrates that mostly respond to overlying water. Diatoms in littoral cores are therefore unique as tools for monitoring recent contamination of lake sediments.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".