Proton Competition and Free Ion Activities Drive Cadmium, Copper, and Nickel Accumulation in River Biofilms in a Nordic Ecosystem
Bibliographic record
Abstract
Biofilms can be used as a biomonitoring tool to determine metal bioavailability in streams affected by mining and other anthropogenic activities. Surface water and biofilm were sampled over two years from rivers located in the vicinity of a mine located in a Nordic ecosystem (Nunavik, Quebec). Biofilm metal content (Cd, Cu, and Ni) as well as a variety of physicochemical properties were determined to examine relationships between metal accumulation and water quality. Among the three metals of interest, copper and nickel had the highest levels of accumulation and cadmium had the lowest. When considering the exposure levels, nickel was the most abundant metal in our sampling sites. Both exposure and accumulation levels were consistent over time. Biofilm metal content was highly correlated to the ambient free metal ion concentration for sites of circumneutral pHs for all three metals. When the surface water pH was below 6, biofilm metal content was much lower than at other sites with similar aqueous metal concentrations of exposure. This apparent protective effect of decreasing pH can be explained by proton competition with dissolved metals for uptake binding sites at the surface of the organisms within the biofilm as described by the Biotic Ligand Model principles. The relationships obtained for Cd and Cu were overlapping those observed in previous publications, indicating strong similarities in metal accumulation processes in biofilms over very large geographical areas. Although more data are needed for Ni, our results show that biofilms represent a promising metal biomonitoring tool.
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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 | 0.000 |
| 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".