Toxicity Identification Evaluation Techniques Isolate Zinc and 6PPD-Q as Causes of Acute Lethality to Rainbow Trout in Municipal Stormwaters
Bibliographic record
Abstract
As prolonged dry periods and extreme rain events are expected to increase due to climate change, the monitoring and treatment of urban stormwaters during rain events becomes of heightened concern. Of particular interest is road runoff in urban areas, which has been found to be acutely lethal to salmonids and frequently contains elevated concentrations of metals and organic contaminants. In this study, samples of road runoff stormwaters were collected in the Metro Vancouver area of British Columbia, Canada and assessed for acute lethality to rainbow trout (Oncorhynchus mykiss). Three of the four stormwaters tested exhibited complete mortality after the 96-h test. The causes of toxicity in samples that exhibited toxicity were characterized by using Toxicity Identification Evaluation (TIE) techniques, and included zinc, as well as an organic constituent. Subsequent investigation of the organic component implicated 6PPD-Q, and the potential contribution of 6PPD-Q to acute toxicity of rainbow trout was assessed by performing TIE techniques on a standard solution of 6PPD-Q in parallel with collected stormwater. Chemical analysis of the solution phase 6PPD-Q concentration using Condensed-Phase Membrane Introduction Mass Spectrometry (CP-MIMS) was used to support toxicity assessments. This is the first study to use the TIE approach to provide a fingerprint for 6PPD-Q toxicity.
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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 | 0.000 | 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) | 0.001 | 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".