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Record W2528751116

Dissipation of Thermal Enrichment of Stormwater Management Ponds

2013· dissertation· en· W2528751116 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2013
Typedissertation
Languageen
FieldEnvironmental Science
TopicHydrological Forecasting Using AI
Canadian institutionsnot available
FundersAlbert Einstein College of Medicine, Yeshiva University
KeywordsStormwater managementStormwaterThermal management of electronic devices and systemsEnvironmental scienceDissipationHydrology (agriculture)Environmental engineeringSurface runoffGeotechnical engineeringEngineeringEcologyPhysicsBiology
DOInot available

Abstract

fetched live from OpenAlex

The intent of this research was to gain a better understanding of the effects of the design parameters on the thermal impact of stormwater management wet ponds. The effect of upland areas on inlet water temperatures of the ponds, thermal design modeling of the ponds, and cooling trenches effects to mitigate stormwater ponds are investigated using data collected from six stormwater ponds in the cities of Guelph and Kitchener, Ontario, Canada. The sensitivity analyses of the developed predictive artificial neural network (ANN) model showed that the rainfall event mean temperatures significantly influenced stormwater temperatures at the inlet of the ponds. The longest pipe length and pipe network density are the two parameters that control the cooling effect of the underground storm sewer system, as opposed to the impervious percentage of the catchment. Concerning the key design parameters of stormwater ponds, larger permanent pool volumes tend to release the warmer water resident in the ponds. Increasing travel path ratio using baffles can lead to less mixing of the water that is resident in the pond with the cooler fresh event runoff and therefore an increase in event mean temperature of outlet. Increasing pond volume from 2000 to 4000 m³ - while keeping all other parameters constant - results in an average increase of 5 °C in event mean temperature at the pond outlet (EMTO); increasing travel path ratio from 0.6 to 1.2 leads to an average increase of 6 °C in EMTO. Regarding the design parameters for stormwater ponds' cooling trench/ rock crib, the results obtained from the sensitivity analyses of the ANN model revealed that the effect of a cooling trench is significantly influenced by the initial temperature of the water and rock in the cooling trench and influent temperature of the water. Reducing the hydraulic depth from 0.8 to 0.3 m in the model shows a 2 °C improvement in the stormwater runoff cooling efficiency of the trench. Increasing the length of the trench from 50 to 100 m in the model confirms a 3 °C improvement in the stormwater runoff cooling efficiency of the trench.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.208
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