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Record W3157237585 · doi:10.3390/en14092421

Influence of Temperature and Moisture Content on Thermal Performance of Green Roof Media

2021· article· en· W3157237585 on OpenAlexafffund
Bohan Shao, Caterina Valeo, Phalguni Mukhopadhyaya, Jianxun He

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of CalgaryUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNanjing Forestry UniversityUniversity of Calgary
KeywordsGreen roofSedumRoofThermal conductivityWater contentMoistureMaterials scienceSubstrate (aquarium)ThermalComposite materialGeotechnical engineeringMeteorologyGeologyHorticultureStructural engineering

Abstract

fetched live from OpenAlex

The influence of moisture content on substrate thermal conductivity at different temperatures was investigated for four different commercially available substrates for green roofs. In the unfrozen state, as moisture content increased, thermal conductivity increased linearly. In the phase transition zone between +5 and −10 °C, as temperature decreased, thermal conductivity increased sharply during the transition from water to ice. When the substrate was frozen, thermal conductivity varied exponentially with substrate moisture content prior to freezing. Power functions were found between thermal conductivity and temperature. Two equally sized, green roof test cells were constructed and tested to compare various roof configurations including a bare roof, varying media thickness for a green roof, and vegetation. The results show that compared with the bare roof, there is a 75% reduction in the interior temperature’s amplitude for the green roof with 150 mm thick substrate. When a sedum mat was added, there was a 20% reduction in the amplitude of the inner temperature as compared with the cell without a sedum mat.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.184

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 communication0.0000.000
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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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 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

Citations17
Published2021
Admission routes2
Has abstractyes

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