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Research on the Application of Ground Source Heat Pump Technology in Timber Structure

2013· article· en· W1996618164 on OpenAlexaboutno aff
Zhi Bian, Tie Cheng Wang, Shi Yong Zhao, Xian Fa Guo

Bibliographic record

VenueApplied Mechanics and Materials · 2013
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsHeat pumpRenewable energyGeothermal energyResource (disambiguation)Geothermal gradientEnergy consumptionEnvironmentally friendlyEnvironmental economicsConsumption (sociology)EngineeringCivil engineeringWaste managementEnvironmental scienceMechanical engineeringComputer scienceHeat exchangerElectrical engineering

Abstract

fetched live from OpenAlex

Ground source heat pump technology is a new type technology of superficially geothermal utilization. By taking Chinese-Canadian cooperation in the energy-saving low-carbon environmental demonstration housing project for example, this paper is to introduce the low-ground heat used in the timber structure. Through analysis of the energy consumption on the integration technology for wooden structure and ground source heat pump, and compared with conventional energy on energy consumption, validated applying renewable energy sources on modern wooden structure will produce significant benefits on economic and environmental, which possesses significant role in promoting that creating a resource-saving and environment-friendly society.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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