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Record W4240159775 · doi:10.1002/9781119181002.ch12

Case Studies

2016· other· en· W4240159775 on OpenAlexaff
Marc A. Rosen, Seama Koohi‐Fayegh

Bibliographic record

VenueGeothermal Energy · 2016
Typeother
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHeat pumpThermal energy storageGeothermal energyGeothermal gradientRenewable heatGeothermal heatingElectricityEnvironmental scienceContext (archaeology)Thermal energyThermalCivil engineeringProcess engineeringEngineeringMeteorologyMechanical engineeringHybrid heatGeologyElectrical engineeringThermodynamicsGeographyGeophysicsPhysics

Abstract

fetched live from OpenAlex

A range of case studies is presented to illustrate the application of geothermal energy systems that utilize the ground for heating and cooling as well as their advantages and disadvantages. The cases consider applications from the residential, commercial and institutional building sectors, as well as relevant utility sector entities involved in electricity generation and district heating and cooling. The case studies illustrate the context in which geothermal energy systems can be employed and assessed, and are based mainly on actual applications and drawn from various sources. The types of geothermal energy systems covered through the case studies include an underground thermal energy storage, a ground and water tank thermal energy storage for heating, a space conditioning with a heat pump and seasonal thermal storage, and an integrated system with a ground-source heat pump, thermal storage, and district energy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.034
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0340.005

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 head0.020
GPT teacher head0.262
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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