Measurement of cyanide in urban snowmelt and runoff
Bibliographic record
Abstract
Ferrocyanide compounds enter the environment as anti-caking additives to road salts. In illuminated aquatic environments, the salts dissociate and form toxic free cyanide, which can then be lost through volatilization. The most common techniques for analysis of cyanide in environmental samples measure total and free (generally weak-acid dissociable, WAD) cyanide species. Cyanide has been detected in urban snow and winter runoff, but its potential impact in aquatic environments is not well understood. Between 2007 and 2009, cyanide was measured in parking lot runoff after deicer application, runoff from an urban snow disposal site and stormwater ponds. Parking lot runoff concentrations were highest, with 42% of samples displaying WAD cyanide and 97% containing total cyanide at concentrations higher than the method detection limit (MDL) of 0.01 mg/L. Smaller proportions of snow disposal site runoff and stormwater pond samples displayed WAD and total cyanide levels above the MDLs. Since the MDLs achieved were higher than guideline levels, the actual number of exceedances could not be determined. While this study indicates that cyanide in road salts poses a potential risk to the aquatic environment, it also highlights the need for more sensitive analytical techniques for such samples.
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.
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 | 0.000 |
| 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) | 0.001 | 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".