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Record W2940914208 · doi:10.1021/acs.est.9b00532

Seasonality of the Water-Soluble Inorganic Ion Composition and Water Uptake Behavior of Urban Grime

2019· article· en· W2940914208 on OpenAlexafffundabout
Alyson M. Baergen, D. J. Donaldson

Bibliographic record

VenueEnvironmental Science & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeasonalityComposition (language)Impervious surfaceNitrateEnvironmental chemistryInorganic ionsChemistryChemical compositionWater solubleEnvironmental scienceAtmosphere (unit)IonGeographyEcologyOrganic chemistryMeteorologyBiology

Abstract

fetched live from OpenAlex

Impervious surfaces, especially in urban environments, are coated with a film composed of a complex mixture of substances, referred to as urban grime. Despite its ubiquity, the factors that dictate urban grime composition are still not well understood. Here, we present the first study of the seasonal variation in composition of water-soluble inorganic ions present in urban grime, performed by analyzing samples collected in Toronto for 4-week intervals over the course of a year. A clear seasonality in the composition is evident, with NaCl dominating in the winter months and Ca2+ and NO3– dominant in the summer. We compare the grime composition to the water-soluble ion composition of PM2.5 and PM10 in order to infer chemistry occurring within the grime and find evidence that chemistry occurring within the urban grime matrix could provide a source of ClNO2 and NH3 to the urban atmosphere. The uptake of water by urban grime also shows a clear seasonality, which may be driven by the changing proportions of nitrate salts and/or oxidized organic compounds over the year.

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)0.000
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.184
Teacher spread0.179 · 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 designBench or experimental
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

Citations23
Published2019
Admission routes3
Has abstractyes

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