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Record W2030588616 · doi:10.1111/jawr.12152

Thermal Pollution Mitigation in Cold Water Stream Watersheds Using Bioretention

2013· article· en· W2030588616 on OpenAlexaff
Daniel L. Long, Randel L. Dymond

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsAmerican Water (Canada)
Fundersnot available
KeywordsBioretentionEnvironmental scienceSurface runoffStormwaterLow-impact developmentThermal pollutionEnvironmental engineeringHydrology (agriculture)PollutionStormwater managementGeotechnical engineeringEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract This study examines the use of bioretention as a strategy to reduce the thermal impact associated with urban stormwater runoff in developing cold water stream watersheds. Temperature and flow data were collected during 10 controlled runs at a bioretention facility located in Blacksburg, Virginia. It was determined that bioretention has the ability to reduce the temperature of thermally charged stormwater runoff received from an asphalt surface. Significant reductions in peak and average temperatures ( p < 0.001) were observed. However, this facility was unable to consistently reduce the temperature below the threshold for natural trout waters in Virginia. The ability of bioretention to reduce runoff volume and peak flow rate also serves to reduce the hydrothermal impact. An average thermal pollution reduction of nearly 37 MJ/m 3 was calculated using an adopted threshold temperature of 20°C. Based on the results of this study, it was concluded that properly designed bioretention systems have the capability to reduce the thermal impact of urban stormwater runoff on cold water stream ecosystems.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 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

Citations26
Published2013
Admission routes1
Has abstractyes

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