Bibliographic record
Abstract
The International Conference on Intelligent Textiles and Mass Customization (ITMC) meets every two years, where the organization rotates among the 5 coordinating countries (Belgium, Canada, France, Japan and Morocco). The 7th edition of the International Conference on Intelligent Textiles and Mass Customization-ITMC 2019 was held at the historical city of Marrakech (MOROCCO), from the 13th to the 15th of November 2019. The ITMC 2019 conference invited guests from various industries and disciplines related to the textile industry. The purpose of the conference is to explore new ideas, effective solutions and collaborative partnerships for business growth throughout the creation of a beneficial synergy between designers, manufacturers, suppliers and end users of all sectors and making full use of this potential. ITMC conference themes are axed on intelligent textiles and mass customization. For 3 days, inspiring speakers from industries, academies, governments and societies shed the light on new chances and challenges, via global statistics and success stories about cutting edge science and technology. The innovation brought to the table of discussion has bloomed through cooperation, policy, education and training and rose via an outstanding interaction between speakers and participants, which has been assured through new IT tools. On behalf of the Conference organizers, we would like to thank all the participants coming from compagnies, universities and research institutions of all around the globe for greeting us with their presence and the lively exchange of ideas and experiences at the ITMC2019 Conference. We are looking forward to seeing you again in the next edition of ITMC 2021 in Canada! List of Editorial board, Keynote Speakers, International Scientific Committee and Organizing Committee are available in this pdf.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.556 | 0.409 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".