28 Editorial Remarks 29 Corporate Members
Bibliographic record
Abstract
has 11,000 members from the USA and many other countries. I have managed large research projects, am active in consulting, and enjoy all topics in geotechnical engineering. I play a lot of tennis, a bit of piano, and used to play rugby and soccer. I have been fortunate to win many awards. the most prestigious being the ASCE Ralph Peck Lecture from the USA and the CGS Geoffrey Meyerhof Award from Canada. I believe that ISSMGE is our international family and that it is our duty to support it and to be active in it. Talking about family, Janet is my wife, Natalie and Patrick are my children. As soon as I became a candidate for the position of President of ISSMGE, I became a “citizen ” of 84 countries. As a “citizen ” of your country, I am interested in helping you with any request you have for changes in ISSMGE. The overarching idea of my candidacy is to engage the members in participating in ISSMGE and shaping its future. I will listen to your requests and try to implement them as much as possible. In the meantime, I propose to work on the following issues.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.040 | 0.037 |
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".