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Record W2810554482 · doi:10.1520/jte20160354

Thermal Conductivity Measurement of Smaller Insulation Specimens Using Standard Heat Flow Meter

2018· article· en· W2810554482 on OpenAlex
Graziela Girardi, Phalguni Mukhopadhyaya, Ehab Zalok

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsUniversity of VictoriaCarleton University
Fundersnot available
KeywordsThermal conductivityMaterials scienceThermal insulationComposite materialMetreHeat flowThermal conductivity measurementHeat transferMetering modeThermalMechanical engineeringThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Thermal conductivity measurement of small insulation specimens is an important necessity during new product development processes or when larger specimens cannot be collected during forensic investigation. The research activities reported in this article examined a new approach for measuring the thermal conductivity of small insulation specimens. Six commercial insulation materials were tested using a standard 300 by 300-mm heat flow meter apparatus with a 150 by 150-mm metering area. The results obtained from these tests were compared with thermal conductivity values of standard size specimens. Subsequently, the experimental results were also critically analyzed using a heat transfer modeling tool HEAT3. The observations presented in this article clearly indicate that the proposed new approach could be applied to measure thermal conductivity of a variety of insulation materials of different dimensions using a standard heat flow meter.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.738
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.163
GPT teacher head0.303
Teacher spread0.140 · 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