Bibliographic record
Abstract
The 4th International Conference on Civil Engineering and Materials Science (ICCEMS 2019) and the 2nd International Conference on Nanomaterials, Materials and Manufacturing Engineering (ICNMMS 2019) were jointly held in Bangkok, Thailand in May 2019. Since their inception, both the ICCEMS and ICNMM have been attracting many delegates from all over the world. This year’s delegates, including the world’s leading scholars, researchers and expert practitioners represented five continents and 23 countries, i.e., USA, Canada, Mexico, Brazil, China, Japan, Korea, Taiwan, Malaysia, Philippines, Thailand, Singapore, India, Indonesia, Russia, Iran, Iraq, France, Germany, Norway, Poland, South Africa, and Nigeria. ICCEMS and ICNMM aim to become premier international conferences for in-depth discussions on the most-up-to-date and innovative ideas, research projects and practices in the field of civil engineering, material sciences and manufacturing engineering. Papers published in the ICCEMS 2019 and ICNMM 2019 proceedings cover various topics from civil and structural engineering to nanomaterials science as well as a broad range of related interdisciplinary subjects. Today, the world develops with unprecedented speed, which is only possible with advances in various fields of sciences and engineering. We are very pleased to contribute to this important global effort. We sincerely hope that the academic community and industrial practitioners will continue to support us in our attempts to provide even more meaningful conferences with numerous critical idea exchanges, diverse opportunities for fruitful networking and future collaborations between the delegates. Warmest regards, Conference Chair Prof. Kyoung Sun Moon Yale University School of Architecture, USA ICCEMS 2019 & ICNMM 2019 Committee
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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 | 0.002 | 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) | 0.563 | 0.406 |
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".