Acute toxicity of storm water associated with de-icing/anti-icing activities at Canadian airports
Bibliographic record
Abstract
Abstract Environment Canada, Transport Canada, and the Airline Transport Association of Canada recently evaluated the use of toxicity bioassays to assist in managing wastewater from aircraft de-icing at Canadian airports. This study evaluated the effectiveness of a suite of rapid screening bioassays to predict the responses of standard regulatory test organisms to storm water associated with de-icing at four Canadian airports. Storm water samples were tested using two standard acute lethality bioassays (rainbow trout [Oncorhynchus mykiss], Daphnia magna) and four rapid screening bioassays (Daphnia IQ™, acute Microtox®, Rotoxkit®, Thamnotoxkit®). Environmental samples (runoff water) and concentrated de-icing/anti-icing chemicals from the clean-up vehicles (sweeper trucks) were collected from each airport and tested. Forty percent (n = 10) of the environmental samples were lethal to trout, and 30% were lethal to D. magna. The IQ and Thamnotoxkit test results were comparable to those of the trout and daphnid bioassays, respectively. Disadvantages associated with the IQ and Thamnotoxkit bioassays included the lack of a standardized quality-assurance/quality-control program, subjectivity in endpoint measurements, and problems in cyst hatching. The limited number of storm-related samples did not permit definitive determination for the causality of toxicity. Results suggest that glycol was not predictive of acute lethality, and that other substances likely contributed to toxicity.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".