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Record W4233292110 · doi:10.1109/icmsao.2019.8880286

Table of Content

2019· article· en· W4233292110 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsnot available
FundersUniversité de SousseNational Technical University of AthensPresidency UniversityEffat UniversityVIT UniversityNational Institute of Technology CalicutKırıkkale ÜniversitesiUniversité de TunisIstanbul Teknik ÜniversitesiIndian Institute of Technology DelhiKing Mongkut's University of Technology North BangkokRégion NormandieFederation University AustraliaAmerican Association for the Surgery of TraumaMemorial University of NewfoundlandMajmaah UniversityUniversiti Malaysia PahangNorges Teknisk-Naturvitenskapelige UniversitetMotilal Nehru National Institute of Technology AllahabadUniversity of BasrahUniversiti Teknologi MalaysiaAswan UniversityTechnische Universität WienAmrita Vishwa Vidyapeetham UniversityUniversité Abdelmalek EssaadiSultan Qaboos UniversityÇukurova ÜniversitesiMonash UniversityKing Faisal UniversityDeakin UniversityNational and Kapodistrian University of AthensLa Trobe UniversityImam Abdulrahman Bin Faisal UniversityAmerican University of SharjahZayed UniversityUniversity of BishaEcole Supérieure des Communications de TunisIndiana State UniversityKing Saud UniversityUniversité Hassan II de CasablancaIndian Institute of Engineering Science and Technology, ShibpurVysoká Škola Ekonomická v PrazeHeriot-Watt UniversityTexas Tech UniversityKing Fahd University of Petroleum and MineralsMinia UniversityJadavpur UniversityUniversitas TelkomFirat Üniversitesi
KeywordsTable (database)Computer scienceDatabase

Abstract

fetched live from OpenAlex

The following topics are dealt with: optimisation; finite element analysis; learning (artificial intelligence); computational fluid dynamics; steel; diseases; numerical analysis; power grids; scheduling; structural engineering computing.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.178
Teacher spread0.169 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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