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Record W2783382971 · doi:10.14447/jnmes.v17i2.433

Potential Applications of Phase-change Materials (PCM) in Building Energy Efficiency

2014· article· en· W2783382971 on OpenAlexvenueno aff
Yancang Li, Guanglei Sang, Huawang Shi

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhase changePhase-change materialEnergy conservationPhase (matter)Waste heatComputer scienceEnergy densityResearch developmentEnergy (signal processing)Environmental scienceArchitectural engineeringMechanical engineeringEngineering physicsEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Phase-change material with high thermal capacity savings density is of great importance in building energy conservation, waste heat recovery, peak load shifting and other fields. We aim to guide the researchers of these problems and provide an underlying basis for the development of this new energy-saving material. We extensively reviewed the development history, classification, research methods and existing problems of the phase change materials system and discussed some potential research topics for the future. This study can promote the related research of the Phase-change materials.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2014
Admission routes1
Has abstractyes

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