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Record W2154498434 · doi:10.14796/jwmm.r227-13

Continuous Distributed Modeling for Evaluation of Stormwater Quality Impacts from Urban Development

2007· article· en· W2154498434 on OpenAlexvenueno aff
Baxter E. Vieux, Jean E. Vieux

Bibliographic record

VenueJournal of Water Management Modeling · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStormwaterSurface runoffEnvironmental scienceLow-impact developmentSedimentStormwater managementWater qualityWater resource managementHydrology (agriculture)Environmental engineeringEnvironmental planningGeologyGeotechnical engineeringEcology

Abstract

fetched live from OpenAlex

Understanding the impact of land use changes on nutrient and sediment loading from stormwater runoff to a water supply reservoir is the motivation for the study referenced in this chapter. Changes in the loading rate and the relative proportion of nutrients, e.g. nitrogen and phosphorus, can have important effects on eutrophication and algae production in the receiving water of the lake. Evaluation of stormwater runoff quantity and quality is performed for the 30.8 km 2 (11.9 mi 2 ) Rock Creek watershed located within the corporate limits of the City of Norman, OK. This watershed is part of the larger drainage area of Lake Thunderbird reservoir, which is operated by the Central Oklahoma Master Conservancy District and supplies drinking water to Norman and two other surrounding communities. The reservoir was constructed by the US Bureau of Reclamation in 1961-1965. The 2001 bathymetric survey determined Lake Thunderbird to have a maximum depth of 58 ft (17.7 m), mean depth of 15.4 ft (4.7 m), surface area of 5,439 acres (2,211 ha) and volume of 105,838 acre-feet (130,180,000 m 3 ). Excessive algae production leads to taste and odor complaints about the finished water product.

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)0.007
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 score0.398
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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 head0.047
GPT teacher head0.296
Teacher spread0.250 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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