Bibliographic record
Abstract
The 8th Edition of International Conference on Intelligent Textiles and Mass Customization (ITMC 2022), which was held at the beautiful city of Montreal (Canada), from the 21st to the 23rd of September 2021, targeted guests from various branches and disciplines related to the textile industry. Its interdisciplinary approach is the key to maximizing the potential and development of textile materials and tools 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 of all sectors and making full use of this potential. 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). On behalf of the Conference organizers, we are honored to invite all interested business, research institutions and organizations from around the globe to participate in a lively exchange of ideas and experiences featured at the ITMC2022 Conference. ITMC conference themes are axed on intelligent textiles and mass customization. During two days, Inspiring speakers from industries, academies, governments and societies have shined the light over new chances and challenges, bringing global statistics and success stories about cutting edge science and technology. The innovation brought to the table of discussion will bloom through cooperation, policy, education and training and rise via an outstanding interaction between speakers and participants, guaranteed through Novel IT tools. Participants were also invited to show their prototypes during the Smart Textiles Salon.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.512 | 0.345 |
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".