Bibliographic record
Abstract
The national conference in computational mechanics (Colloque national en calcul des structures) is organised every two years by the French Computational Structural Mechanics Association (CSMA). CSMA is affiliated to the “Association Française de Mécanique” (AFM), the International Association for Computational Mechanics (IACM) and the European Community on Computational Methods in Applied Sciences. This regular and well-established event is the unique national meeting dedicated to this field. Since its inception in 1993, this Conference attracts an ever-increasing audience of junior and senior scientists, engineers and doctoral students. This 10th edition of the CSMA meeting, held on 9–13 May 2011 at the village of Giens (near Toulon, France), was organised by the LMGC laboratory (University of Montpellier, France). It gathered about 350 participants from all French research centres active in computational mechanics, and also about 25 participants from Belgium, Canada, Germany, the Netherlands, UK, Spain, Switzerland, USA and a few other countries. Moreover, about 12% of the delegates came from industry. This special issue of the European Journal of Computational Mechanics gathers 21 papers selected from around 280 communications presented at the Conference. These contributions have been selected on the basis of quality, while also attempting to provide a general overview of current research in the field of computational mechanics. We warmly thank all the authors of the papers featured in this special issue.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.045 |
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".