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Record W4377136786 · doi:10.1016/j.ijft.2023.100380

Effect of using phase change materials on thermal performance of passive solar greenhouses in cold climates

2023· article· en· W4377136786 on OpenAlexaff
Mohamed Ismail, İbrahim Dinçer, Yusuf Biçer, M. Ziad Saghir

Bibliographic record

VenueInternational Journal of Thermofluids · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGreenhouse Technology and Climate Control
Canadian institutionsToronto Metropolitan UniversityOntario Tech University
Fundersnot available
KeywordsPassive solar building designGreenhouseEnvironmental sciencePhase changeThermalSolar energyThermal energy storageAtmospheric sciencesMaterials sciencePhase-change materialMeteorologyEngineering physicsEnvironmental engineeringEngineeringGeographyThermodynamicsAgronomyPhysics

Abstract

fetched live from OpenAlex

Passive solar greenhouses are crucial for sustainable agriculture in cold regions, but they face challenges in temperature regulation, especially at night when temperatures drop down significantly. Phase Change Materials (PCMs) appears to be a potential solution to improve the thermal stability by storing and releasing large amounts of thermal energy during phase changes. In the present study, it is obtained that incorporating PCMs into the north wall of a passive solar greenhouse helps extend the growing season by up to 48 days, increase the average temperature during the growing season from 12.1 °C to 24.8 °C, and enable a wider range of crops to be cultivated. This study provides valuable insights into the feasibility of using PCMs in passive solar greenhouses to improve temperature regulation and energy efficiency in cold climates.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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

Citations28
Published2023
Admission routes1
Has abstractyes

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