Proceedings of the Nineth International Conference on Geotechnique, Construction Materials and Environment (GEOMATE 2019)
Bibliographic record
Abstract
Proceedings of the Ninth International Conference – GEOMATE 2018: Geotechnique, Construction Materials and Environment, Tokyo, Japan, 20-22 November, 2019. \n \nWelcome to the GEOMATE 2019 - The Ninth International Conference on Geotechnique, Construction Materials and Environment, GEOMATE 2019, will be held in Hotel Continental Fuchu, Tokyo, Japan in conjunction with the Japan Association for Pulling-out Existing Piles, Mie University Research Center for Environmental Load Reduction, The GEOMATE International Society, The Society of Materials Science, Japan, The Useful Plant Spread Society, Japan, Glorious International, AOI Engineering, HOJUN, CosmoWinds and Beppu Construction, Japan. \n \nOn Friday 11 March 2011, at 14:46 Japan Standard Time, the northeast of Japan was struck and severely damaged by a series of powerful earthquakes which also caused a major tsunami. This conference was first dedicated to the tragic victims of the Tohoku-Kanto earthquake and tsunami disasters. The Geomate 2018 conference covers three major themes with 17 specific themes including: \n \n● Advances in Composite Materials \n● Computational Mechanics \n● Foundation and Retaining Walls \n● Slope Stability \n● Soil Dynamics \n● Soil-Structure Interaction \n● Pavement Technology \n● Tunnels and Anchors \n● Site Investigation and Rehabilitation \n● Ecology and Land Development \n● Water Resources Planning \n● Environmental Management \n● Public Health and Rehabilitation \n● Earthquake and Tsunami Issues \n● Safety and Reliability \n● Geo-Hazard Mitigation \n● Case History and Practical Experience \n \nAlike earlier conferences, this year we have received many paper submissions from different countries all over the world, including Australia, Bahrain, Bangladesh, Brazil, Canada, Colombia, Czech Republic, Egypt, France, India, Indonesia, Iran, Iraq, Israel, Japan, Kazakhstan, Lebanon, Malaysia, Nigeria, Pakistan, Philippines, Russia, Saudi Arabia, Singapore, South Korea, Taiwan, Thailand, Tunisia, Uganda, United Kingdom and Vietnam. The technical papers were selected from the vast number of contributions submitted after a review of the abstracts. The final papers in the proceedings have been peer reviewed rigorously and revised as necessary by the authors. It relies on the solid cooperation of numerous people to organize a conference of this size. Hence, we appreciate everyone who supports as well as participate in this joint conference. \n \nLast but not least, we would like to express our gratitude to all the authors, session chairs, reviewers, participants, institutions and companies for their contribution to GEOMATE 2019. We hope you enjoy the conference and find this experience inspiring and helpful in your professional field. We look forward to seeing you at our upcoming conference next year. \n \nBest regards, \nProf. Zakaria Hossain, Prof. Mie University, Japan \nProf. Dr. Shinya Inazumi, Shibaura Institute of Technology, Japan \nDr. Jim Shiau, University of Southern Queensland, Australia
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.270 | 0.152 |
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".