Toxicity identification evaluation techniques isolate zinc and 6PPD-Q as causes of acute lethality to rainbow trout in road runoff
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The buildup of pollutants on impervious surfaces, and their subsequent flush into the environment within stormwater, could worsen with expected increases in prolonged dry periods and extreme rain events due to climate change. As such, the monitoring and treatment of urban stormwaters is becoming a high priority. 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 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 100% mortality in the 96-hr test. Stormwater toxicity was demonstrated to be reduced by treatment in a rain garden. Phase I Toxicity Identification Evaluation (TIE) techniques initially identified a metal as the cause of toxicity in one stormwater, which was determined to be zinc after Phase II/III TIE testing. The second stormwater sample revealed an organic constituent to be responsible for toxicity, and subsequent TIE testing implicated N‐(1,3‐dimethylbutyl)‐N′‐phenyl‐p‐phenylenediamine‐quinone (6PPD-Q). The potential contribution of 6PPD-Q to toxicity was assessed by performing TIE techniques on a standard solution of 6PPD-Q in parallel with the stormwater. Chemical analysis of 6PPD-Q using Condensed-Phase Membrane Introduction Mass Spectrometry was used to support toxicity assessments. This is the first study to use the TIE approach to provide a toxicity profile for 6PPD-Q.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it