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Record W2130329062 · doi:10.2166/wqrjc.2011.023

Chloride transport and related processes at a municipal snow storage and disposal site

2011· article· en· W2130329062 on OpenAlexaffabout
Kirsten Exall, Jiří Maršálek, Quintin Rochfort, Steven Kydd

Bibliographic record

VenueWater Quality Research Journal · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsMeltwaterSnowmeltSnowEnvironmental scienceChlorideHydrology (agriculture)Snow removalStormwaterEnvironmental engineeringEnvironmental chemistryChemistrySurface runoffGeologyMeteorologyGeographyEcology

Abstract

fetched live from OpenAlex

The practice of used snow disposal has evolved from indiscriminate snow dumping to storage and meltwater management at engineered snow disposal sites. The Town of Richmond Hill, Ontario, Canada, constructed such a snow disposal site in 2003. Environment Canada and the Town of Richmond Hill formed a partnership to investigate the operation of the Richmond Hill Snow Storage Facility (RHSSF) with respect to snowmelt flows, fluxes of chemicals contained in snowmelt and direct effects on the receiving water. Results of chloride monitoring during the winter of 2007 are presented here. Roughly 16 tonnes of chloride passed through the snow disposal facility in 2007, which in itself reflects only a small proportion of the total salt applied to roads that winter. The highest concentrations of chloride were present in early melt, with 50% of the chloride released within the first 30% of the meltwater. The remainder of the chloride was released in lower concentration, higher volume melt later in the season. As expected, conductivity measurements at the outlet of the stormwater pond indicate that the meltwater management system served to delay and dilute the chloride released, but not to remove the pollutant.

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)0.004
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 score0.025
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.321
Teacher spread0.243 · 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

Citations15
Published2011
Admission routes2
Has abstractyes

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