MétaCan
Menu
Back to cohort
Record W4293542235 · doi:10.1016/j.egyr.2022.07.173

Analysis of combined low-level indicators for the hot-season performance of roof components

2022· article· en· W4293542235 on OpenAlexaff
Chiara Lodi, Alberto Muscio, Paolo Tartarini, Hashem Akbari

Bibliographic record

VenueEnergy Reports · 2022
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsTRNSYSRoofTransmittanceCeiling (cloud)Environmental scienceThermal transmittanceThermalSolar energyCooling loadSolar gainMeteorologyMaterials scienceStructural engineeringAir conditioningEngineeringMechanical engineeringThermal resistanceOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

A single performance indicator, the solar transmittance factor (STF), has been proposed in previous works, together with the derived solar transmittance index (STI). It is aimed at evaluating the summer performance of the roofing system and allowing the selection of the most effective mix of surface and mass properties. It is easily calculated from low-level indicators such as U-value, module of periodic thermal transmittance, and solar reflectance. In the present work, the correlation between STF and the cooling energy demand, integrated over a reference period, was studied, as well as the peak of ceiling temperature increase with respect to the indoor temperature, relevant for thermal comfort. In particular, the thermal behavior of different roof types with variable insulation was calculated numerically by TRNSYS 17 for a wide set of locations and environmental conditions. Unlike other commonly used indicators, to which the analysis has been extended, a strong correlation with STF was found for both cooling energy demand and ceiling temperature rise.

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: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.191
Teacher spread0.182 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEnergy ReportsSame topicBuilding Energy and Comfort OptimizationFrench-language works237,207