Bibliographic record
Abstract
Dear Authors and Participants of ICCEA 2019, On behalf of the conference committees, I am pleased to declare that we had really successful three days of 2019 2nd International Conference on Civil Engineering and Architecture (ICCEA 2019), which was held jointly in Seoul, South Korea during September 21-23, 2019, sponsored by Seoul National University. Again, the conference was of great success, totally beyond my expectation, having a diverse group of expertise and backgrounds. Not to mention that participants are geographically diverse. I was glad to confirm that such diverse expertise and participants were somehow connected, forming a coherent conference environment and creating synergistic learning and sharing. ICCEA is the premier forum for the presentation of new advances and research results in the fields of theoretical, experimental, and practical civil engineering and architecture. The conference indeed brought together leading researchers, engineers and architects in the domain of interest from around the world. ICCEA 2019 were composed of 6 oral parallel sessions, 4 keynote speeches delivered respectively by Prof. Ashraf El Damatty, The University of Western Ontario, Canada; Prof. Youngjin Lee, Boston Architectural College & Sasaki Associates, Inc., USA; Prof. T.C. Pong, Hong Kong University of Science and Technology, Hong Kong; and Prof. Patrick Safran, Incheon National University, South Korea; and finally, I would like to thank you to all of our conference committees and participants for always being supportive to the conferences and coming to Seoul during your busy schedule. As expected, the conference was proven to be intellectually stimulating to all of us. Hope all enjoyed the conference, the food, the hospitality, as well as the beautiful and charming environment of Seoul. I look forward to seeing you again in Beijing in Sept. 2020! Oct. 1, 2019 Dr. Thomas Kang Professor, Seoul National University, South Korea Conference Chair, ICCEA 2019 tkanq@snu.ac.kr
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 | 0.014 |
| 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) | 0.447 | 0.336 |
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".