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Record W2921445092 · doi:10.2166/wqrj.2000.042

Review of Water Quality Impacts of Winter Operation of Urban Drainage

2000· article· en· W2921445092 on OpenAlex
Gary L. Oberts, Jiří Maršálek, Maria Viklander

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Quality Research Journal · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsSnowmeltMeltwaterEnvironmental scienceWater qualityStormwaterSurface runoffSnowDrainageUrban runoffDeposition (geology)Hydrology (agriculture)Environmental engineeringMeteorologyGeographyGeologySedimentEcology

Abstract

fetched live from OpenAlex

Abstract Urban snowpacks accumulate large quantities of solids and contaminants, which originate from such sources as airborne fallout, vehicular deposition, and applied grit and salt. Both contaminants and solids may be quickly released during the periods of snowmelt and, consequently, melting contaminated snow in urban areas in cold climates has the potential to substantially impact the water quality of receiving water bodies. Although data on the water quality impacts of meltwater are relatively scarce, instances of toxicity of the highly concentrated first flush and deterioration of the receiving water quality by winter discharges of solids and chemicals have been documented. Common rainfall-runoff management techniques do not usually address snowmelt impacts because of the cold weather effects on biological systems and physical processes. Further research on adaptation of conventional Stormwater management techniques to cold climate conditions is needed.

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 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)0.015
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.000

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 head0.109
GPT teacher head0.408
Teacher spread0.299 · 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