Calibration of a laboratory-based chronic toxicity model to nickel effects on stream invertebrates in the field
Bibliographic record
Abstract
Predicting effects of metals on stream invertebrate communities can be hindered by spatial and temporal variation in toxicity-modifying factors, a paucity of laboratory toxicity data for stream taxa (mainly insects), and variation in benthic invertebrate community structure related to habitat and factors other than the stressor of interest. We addressed these challenges by combining laboratory-based chronic toxicity data with field-based biological monitoring data to build a lab-to-field stressor-response relationship. A laboratory-based toxicity model for Ceriodaphnia dubia was used to translate field nickel (Ni) concentrations into predicted toxicity, and a quantile regression model of field data from a mine-affected watershed was then used to describe the limiting effect of Ni toxicity on the benthic invertebrate community. Many taxa showed no evidence of Ni effects up to the highest studied exposure (30 µg/L dissolved Ni, or 92% effect to C. dubia reproduction). The most sensitive metric was percent Ephemeroptera, reflecting declines in abundance of some sensitive mayflies (e.g., Baetis, Epeorus) and concurrent increases in abundance of some tolerant non-mayfly taxa (e.g., Rheocricotopus, Eukiefferiella) across the gradient of Ni toxicity in the field. The field 10% effect concentration for percent Ephemeroptera occurred at a 22% effect of Ni to C. dubia reproduction. This finding supports previous estimates of 20% as a critical effect size in laboratory test organisms that could be predictive of discernible effects on sensitive invertebrates in the field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".