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Record W1149335751 · doi:10.14796/jwmm.r228-01

Interactions of Phosphorus with Anthropogenic and Engineered Particulate Matter as a Function of Mass, Number and Surface Area

2008· article· en· W1149335751 on OpenAlexvenueno aff
Jong Yeop Kim, Jia Ma, Kim M. Howerter, Giuseppina Garofalo, John J. Sansalone

Bibliographic record

VenueJournal of Water Management Modeling · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsParticulatesEnvironmental sciencePhosphorusFunction (biology)Environmental chemistryAtmospheric sciencesEcologyGeologyMaterials scienceChemistryBiologyMetallurgy

Abstract

fetched live from OpenAlex

Particulate matter (PM) is ubiquitous in modern urban environments, generated by most anthropogenic activities. Traffic activities, for example, generate significant loads of PM, as do land-disturbing and construction activities. There are also significant sources of biogenic particulate loads in the modern urban environment, such as vegetation. While the ubiquity of PM is generally recognized, the sources, characteristics, transport, treatment and impact of particulate matter in urban runoff (rainfall or snow) continue to be the subject of lively debate. Such knowledge is fundamentally important for modeling urban runoff particulates, particularly regarding the transport and fate of chemicals such as phosphorus and metals. As modeling capabilities begin to couple hydrology, chemistry and PM, improved models such as the USEPA stormwater management model (SWMM) require defensible data and algorithms. Three common topics of debate and new experimental results are presented. The first concerns particle size distributions (PSDs) in urban runoff.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.522

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2008
Admission routes1
Has abstractyes

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