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Record W4288003066

International Conference on Intelligent Textiles & Mass Customisation – ITMC 2019

2019· article· en· W4288003066 on OpenAlexaboutno aff
Ivana Schwarz, Dragana Kopitar

Bibliographic record

VenueHrčak Portal of scientific journals of Croatia (University Computing Centre) · 2019
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

The 7th edition of the International Conference on Intelligent Textiles and Mass Customization – ITMC 2019 was held in the Marrakech, Morocco, from 13th to 15th November 2019. The Conference was organized by the Higher School of Textile and Clothing Industries (ESITH), in partnership with Ghent University in Belgium, the National School of Arts and Textile Industries (ENSAIT) in France, Shinshu University in Japan and CTT Group of Canada. The ITMC 2019 conference interdisciplinary approach is the key to maximizing the potential and development of textile materials for various applications. The purpose of the conference is to explore new ideas, effective solutions and collaborative partnerships for business growth by catalyzing the creation of a beneficial synergy between designers, manufacturers, suppliers and end-users from all sectors and making full use of this potential. The themes of the ITMC conference focus on smart textiles and mass personalization: advanced manufacturing, comfort, digital tools, design methodologies, connected composites, mass customization, e-textile and e-commerce, smart and functional textiles, education and training, funding opportunities, supply chain management and logistics, sustainable production and recycling.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.986

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.001

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 head0.046
GPT teacher head0.281
Teacher spread0.235 · 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 designBench or experimental
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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