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Record W2783841485

SIMULTANEOUS IDENTIFICATION OF UNKNOWN INITIAL TEMPERATURE AND HEAT SOURCE

2016· article· en· W2783841485 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.

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

Bibliographic record

VenueLanzhou University Institutional Repository · 2016
Typearticle
Languageen
FieldMathematics
TopicNumerical methods in inverse problems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMathematicsHeat equationUniquenessOperator (biology)Identification (biology)Thermal conductionBenchmark (surveying)Initial value problemApplied mathematicsConjugate gradient methodMathematical optimizationMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

We investigate in this paper an ill-posed backward heat conduction problem of determining the unknown initial temperature and heat source from given observation at a fixed internal location and the solution value at terminal time. Unlike the classical single parameter identification problems, this ill-posed problem requires the determination of two independent unknown functions from scattered measurement of noisy data. Proof on the uniqueness of the solution is obtained by transforming the original heat conduction equation into an operator equation of the first kind. A new algorithm for the construction of the solution to the backward problem is derived by using the Landweber iteration method for the solution of the corresponding conjugate operator equation. Numerical verification on the efficiency and accuracy of the proposed algorithm is performed by solving several benchmark examples. The proposed method is readily extendable to solve more general multi-parameter identification problems.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.021
GPT teacher head0.272
Teacher spread0.250 · 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