Using biofilms for monitoring metal contamination in lotic ecosystems: The protective effects of hardness and pH on metal bioaccumulation
Bibliographic record
Abstract
Biofilms can make good bioindicators and biomarkers, offering a convenient tool to monitor metal contamination in streams that results from mine tailing sites. Biofilm metal content (Cu, Zn, Cd, Pb) as well as diatom diversity and the presence of teratologies (diatom abnormalities) were determined for biofilms from rivers with a variety of physicochemical properties across a metal contamination gradient. The results of metal accumulation were highly consistent from year to year, with significant relationships between calculated free metal ion concentrations and biofilm metal contents for samples from different rivers. This indicates the "universal nature" of the metal accumulation process in biofilms. The authors observed that protons and major cations protected against metal accumulation. A very low number of diatom taxa were found at the most contaminated sites, and the highest proportions of deformities were observed at these sites. However, it was difficult to distinguish the effect of metal contamination from the effect of other parameters, especially pH. The results suggest that the development of biofilm-based proxies for metal bioavailability is useful and that incorporation of the effects of hardness and pH in this metal contamination monitoring tool is important. Environ Toxicol Chem 2016;35:1489-1501. © 2015 SETAC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".