MétaCan
Menu
Back to cohort
Record W4321849674 · doi:10.1061/jwrmd5.wreng-5869

The Impact of Ground Heat Capacity on Drinking Water Temperature

2023· article· en· W4321849674 on OpenAlexaff
Sarai Díaz, Joby Boxall, Louis Lamarche, Javier González

Bibliographic record

VenueJournal of Water Resources Planning and Management · 2023
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersAgencia Estatal de InvestigaciónEuropean Regional Development FundEngineering and Physical Sciences Research CouncilJunta de Comunidades de Castilla-La ManchaUniversidad de Castilla-La ManchaMinisterio de Ciencia e Innovación
KeywordsWork (physics)Environmental scienceGroundwaterHeat transferMechanicsGeotechnical engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Temperature is known to impact physical, chemical, and biological processes in Drinking Water Distribution Systems (DWDS), but it is rarely considered or modeled. This research evaluates the impact of considering a finite heat capacity for the ground, which has been assumed infinite in previous DWDS research. The aim of this work is to explore and quantify the region where the difference between considering infinite or finite heat capacity for the ground is significant, i.e., the distance over which water-ground heat transfer interaction is important. A detailed model comparison is carried out for key pipe materials, diameters, and hydraulic conditions. Temperature effects are found to exist for up to tens of kilometers (i.e., several hours) into the DWDS. Whereas the differences found were only a few degrees Celsius, this will affect all reaction rates, such as chlorine decay, and is at the start of the DWDS so will impact the entire downstream network. This work highlights the importance of considering temperature in DWDS, and in particular the finite heat capacity of the ground, in ensuring the provision of safe drinking water.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.187

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 science0.0000.000
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 head0.018
GPT teacher head0.248
Teacher spread0.229 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Water Resources Planning and ManagementSame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207