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Record W2159455056 · doi:10.2166/wqrjc.2011.022

Measurement of cyanide in urban snowmelt and runoff

2011· article· en· W2159455056 on OpenAlexaff
Kirsten Exall, Quintin Rochfort, Jiří Maršálek

Bibliographic record

VenueWater Quality Research Journal · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsCyanideSurface runoffEnvironmental scienceEnvironmental chemistryStormwaterSnowmeltSnowEnvironmental engineeringChemistryEcologyMeteorologyGeographyInorganic chemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.175
GPT teacher head0.345
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2011
Admission routes1
Has abstractyes

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