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Record W2969772214 · doi:10.1051/meca/2019026

Numerical identification of the thermal conductivity tensor and the heat capacity per unit volume of an anisotropic material

2019· article· en· W2969772214 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMechanics & Industry · 2019
Typearticle
Languageen
FieldMathematics
TopicNumerical methods in inverse problems
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsThermal conductivityTensor (intrinsic definition)Conjugate gradient methodAnisotropyMaterials scienceFinite element methodFinite volume methodHeat capacityRepresentative elementary volumeMechanicsMathematical analysisThermodynamicsMathematicsGeometryPhysicsMathematical optimizationComposite materialOptics

Abstract

fetched live from OpenAlex

In this paper, an inverse approach based on gradient conjugate method for thermal conductivity tensor and heat capacity per unit volume measurement is summarized. A suitable analysis is done for the mesh in finite element method and for the time steps for the time integration. For a composite material, it is shown the importance to identify the thermal conductivity tensor components in the principal axes.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.058
GPT teacher head0.302
Teacher spread0.244 · 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