Bibliographic record
Abstract
Preface GeoShanghai is a series of international conferences on geotechnical engineering held in Shanghai quadrennially. The conference was inaugurated in 2006 and was successfully held in 2010, 2014 and 2018, with more than 1600 participants in total. Since the last conference, the geotechnical community has witnessed many advances both in fundamental understanding and engineering practices. To demonstrate the latest developments and promote collaborations in geotechnical engineering and related fields, we launch the 5th GeoShanghai International Conference to be held in May 2024. There has been a growing emphasis on sustainable and long-lasting ground improvement solutions in recent years. Addressing these challenges requires innovation and collaboration across disciplines. As a result, several innovative techniques have emerged in research and practice. These can include methods such as intelligent compaction, jet grouting, stiffened deep mixing piles and the use of geosynthetics. These techniques are employed to effectively increase the bearing capacity, reduce settlement, control seepage, improve overall stability, and minimize environmental impact. This volume introduces the latest research outcomes around the world in the following fields of ground improvement, deep excavation and retaining structures, and shafts and deep foundations. The contents help us realize the latest academic and technical developments in these areas and better meet the evolving engineering needs in the future. This volume received a total of 46 papers from countries such as China, United States, Canada, India and others, totaling 42 papers accepted. All papers were reviewed and the accepted papers will be submitted to IOP Conference Series: Earth and Environmental Science indexed by Scopus. Cordially yours, Cheng Lin, Xianda Shen, Douglas Cortez, Shengjie Rui, Linlong Mu The editors of Volume 7: Ground Improvement and Foundations
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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 | 0.001 | 0.010 |
| 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 | 0.004 |
| Insufficient payload (model declined to judge) | 0.503 | 0.382 |
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".