Full-Scale Testing and Foundation Design : Honoring Bengt H. Fellenius
Bibliographic record
Abstract
Full-Scale Testing and Foundation Design, GSP XXX, honors Bengt H. Fellenius, Dr.Tech., P.Eng., for his noteworthy efforts to advance foundation engineering research, design, construction, and testing practices. From the beginning in Sweden of his 50-year career as a geotechnical engineer through nearly two decades as a professor of civil engineering at the University of Ottawa, Dr. Fellenius has made outstanding contributions in the international arena to the development and advancement of innovative approaches and practical methods for incorporating full-scale testing in foundation design, especially for deep foundations. This collection contains 51 papers on the use of full-scale testing to enhance the design of foundations. Eighteen papers are reprints of landmark articles authored or co-authored by Dr. Fellenius. Thirty-three additional peer-reviewed papers written especially for this collection present test methods for various types of foundations and describe useful applications in a diversity of situations. These papers offer findings from interesting projects and document valuable lessons learned by some of the leading experts in the field of foundation engineering from around the world. Full-Scale Testing and Foundation Design contains technical and practical information that will be valuable to geotechnical engineers, foundation specialists, researchers, and construction engineers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".