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Record W2886470930 · doi:10.29007/2km4

Investigation and feasibility study of ground source heat pump for office buildings in China

2018· paratext· en· W2886470930 on OpenAlexfundno aff
Lei Yong, Hongwei Tan, Yue Li

Bibliographic record

VenueEasyChair preprint · 2018
Typeparatext
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsHeat pumpAdaptabilityChinaCivil engineeringField surveyEnvironmental scienceArchitectural engineeringEngineeringMechanical engineeringGeography

Abstract

fetched live from OpenAlex

The adoption of ground-source heat pump (GSHP) in building sector for space heating/cooling, has increased rapidly during the past several decades around the world, especially in China. Meanwhile, it also exposed a lot of problems such as poor economic and energy efficiency, and even failure to operate properly. The purpose of this paper is to post-assess the application of existing GSHP projects, and to analyze the feasibility of ground-coupled heat pumps(GCHP) applied to office buildings in different climatic zones in China. More than one hundred GSHP cases are investigated through literature surveys and field data collection. Based on the post-evaluation results, a model for assessing the adaptability of GCHP is established. The feasibility evaluation of GCHP applied to office buildings are carried out, and key indicators, such as available capacity, energy savings, and cost-efficiency, etc. are presented among different climatic zones in China.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.285
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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