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Record W4288852838 · doi:10.54570/atpet2019/02/03/0048

Thermal Comfort Measurement for Wet Floor Cooling System

2019· article· en· W4288852838 on OpenAlexaff
Pavol Mičko, Andrej Kapjor, Dávid Hečko, Marián Pafčuga

Bibliographic record

VenueAdvances in Thermal Processes and Energy Transformation · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Regina
FundersKultúrna a Edukacná Grantová Agentúra MŠVVaŠ SRAgentúra na Podporu Výskumu a Vývoja
KeywordsHeat pumpStorage heaterRadiant heatingThermal comfortPassive coolingThermal insulationRenewable heatEnvironmental scienceAir source heat pumpsRadiant coolingMechanical engineeringEnergy consumptionCladding (metalworking)ThermalHybrid heatArchitectural engineeringEngineeringMaterials scienceMeteorologyElectrical engineeringHeat exchangerComposite material

Abstract

fetched live from OpenAlex

The trend of constant increase in energy prices can be observed especially on the increased demands on the thermal insulation properties of building structures of buildings. According to European Directive 2010/31 / EU, since 2019 only buildings that meet the energy standards of near zero buildings have to be designed. In practice, the design of the building takes into account, in particular, the shape of the building, its cladding, but also the method and technology for heating, cooling and hot water production. In the case of a family house is considered a specific annual consumption of heat for heating up to 20 kW.h.m-2 floor area. A popular way to achieve low heat consumption is to select an efficient heat source - a heat pump. It is best to combine a heat pump with a heating system with a low temperature gradient. The combination of heat pump and radiant floor heating is very popular. Modern heat pumps also come with the possibility of reversible operation and serve as a source of cold. The following article will therefore address floor radiant cooling and its effect on thermal comfort.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.565
Threshold uncertainty score0.492

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 communication0.0000.001
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 head0.006
GPT teacher head0.190
Teacher spread0.183 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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