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Record W2305653498 · doi:10.14796/jwmm.r236-09

Impervious Cover Variability in Urban Watersheds

2010· article· en· W2305653498 on OpenAlexvenueno aff
Celina Bochis, Robert E. Pitt

Bibliographic record

VenueJournal of Water Management Modeling · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsImpervious surfaceCover (algebra)Environmental scienceHydrology (agriculture)GeographyGeologyEngineeringGeotechnical engineeringEcologyBiology

Abstract

fetched live from OpenAlex

An urban area inventory for watershed development conditions should be part of any comprehensive stormwater management plan, when the goal is to understand the sources of pollution and the magnitude of the expected runoff. The watershed inventory would, therefore, assist in the selection of the most beneficial stormwater control practices. The type of urban development in an area can have a major impact on the local hydrology and water environment. This inventory can therefore be used to support many decision making activities and to increase the success of local stormwater monitoring. Past studies Increasing levels of impervious surfaces associated with urbanization result in higher volumes of runoff with higher peak discharges, shorter travel times, and more severe pollutant loadings. Urban imperviousness is an important indicator for urban watersheds in measuring the impact of land development on drainage systems and aquatic life (Schueler 1994). However, there are many different types of impervious surfaces, and their direct connectivity to the drainage system is an important attribute affecting stormwater runoff. The purpose of this chapter is to show the measured variability associated with land surface covers for different land uses in a large urban area in the state of Alabama.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 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

Citations2
Published2010
Admission routes1
Has abstractyes

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