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Record W2106450107 · doi:10.14796/jwmm.r225-18

Monitoring the Performance of a Construction Sediment Pond

2006· article· en· W2106450107 on OpenAlexafffundvenue
Lindsay Pyatt, James Li

Bibliographic record

VenueJournal of Water Management Modeling · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsToronto Metropolitan University
FundersMinistry of Education, IndiaMinistry of EnvironmentGovernment of CanadaNational Water Research Institute
KeywordsSedimentSustainabilityPollutionEnvironmental scienceGovernment (linguistics)Water pollutionEnvironmental planningUrban sustainabilityWater resource managementEnvironmental resource managementHydrology (agriculture)BusinessGeologyEcology

Abstract

fetched live from OpenAlex

Construction sites are significant water pollution sources in urban watersheds.In cooperation with the Government of Canada's Great Lakes Sustainability Fund (GLSF), Town of Richmond Hill (RH), Toronto and Regions Conservation Authority (TRCA), Department of Fisheries and Oceans (DFO), Ontario Ministry of Environment (MOE), National Water Research Institute (NWRI), a Ryerson University's research team monitored a sediment control pond in the Town of Richmond Hill in 2002.The objectives of the monitoring study are to (i) characterize runoff entering and leaving the sediment control pond during the construction phase; and (ii) evaluate the sediment removal efficiency of the pond.The pond experienced high removal rates for suspended solids during the monitoring period.However, suspended solids exiting the pond during some events were high which might cause downstream fishery impact.This study has demonstrated that strictly erosion and sediment control requirements are necessary for construction sites to prevent downstream water quality impacts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.193
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2006
Admission routes3
Has abstractyes

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