Assessment of Nickel Contamination in Lakes Using the Phantom Midge <i>Chaoborus</i> As a Biomonitor
Bibliographic record
Abstract
Nickel (Ni) can be present in concentrations of concern in waters near mining and industrial sites. We tested species of the phantom midge Chaoborus as a biomonitor for this trace metal by collecting water and Chaoborus larvae from 15 lakes located along a Ni gradient mainly in the vicinity of smelters located in Sudbury, ON, Canada. We measured pH, trace metals, major ions, as well as inorganic and organic carbon concentrations in lakewater for use in calculating ambient metal speciation using the Windermere Humic Aqueous Model (WHAM). Nickel concentrations in Chaoborus species varied widely among our study lakes and could be related to concentrations of the free Ni2+ ion in lakewater if competitive interactions with hydrogen ions (H+) were taken into account We verified this inhibitory effect in the laboratory by exposing Chaoborus punctipennis to constantfree Ni2+ ion concentrations at various H+ ion concentrations. As expected, larvae exposed to high concentrations of H+ ions accumulated less Ni. Overall, our results suggest that Chaoborus larvae would be an excellent biomonitor for Ni in lakewater and as such would be a useful component of risk assessment strategies designed to evaluate Ni exposure to aquatic organisms in lakes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".