2019 3rd International Conference on Manufacturing Technologies (ICMT)
Bibliographic record
Abstract
Preface The 2019 3rd International Conference on Manufacturing Technologies (ICMT2019) was held in held in San Francisco, USA during January 4-7, 2019. This volume consists of selected papers presented at ICMT 2019, which are addressing diverse topics related to recent trends in the inter-related areas of Mechanical, Manufacturing and Materials Engineering. The conference provides opportunities for the delegates to exchange new ideas and application through face-to-face discussions, to establish business or research relations and to find global partners for future collaborations. We hope that you will find its contents interesting, stimulating and educative. ICMT 2019 is organized by the South Asia Institute of Science and Engineering. The technical objective of the conference is to address key issues associated with science and technology in these rapidly evolving fields of research and to promote contact between basic researcher and technological needs for real industrial applications. The conference program offers invited, oral and poster presentations. The invited talks are focused on the potential application of mechanical, manufacturing, mechatronics and materials engineering in various fields. Many excellent manuscripts were selected after peer review and were recommended for publication in this volume. Topics cover all aspects of the areas listed above. I would like to thank the members of the conference committee, the reviewers who spared their valuable time, for their advice which have certainly helped to improve the quality, accuracy, relevance and to enhance the technical contributions of each paper selected for this conference program and volume for publication. And thank you to each author and participant who has contributed to the success of the conference. Prof. Dr. Ridha Ben Mrad University of Toronto, Canada January/4-7/2019
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.155 |
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".