Bibliographic record
Abstract
The Fluid Mechanics Conference (FMC) is organized biennially under the auspices of the Fluid Mechanics Committee of the Polish Academy of Sciences, the Polish Society of Theoretical and Applied Mechanics, and the European Research Community on Flow, Turbulence, and Combustion (ERCOFTAC). The FMC aims to bring together scientists from various countries, strengthen connections between experimental, computational, and theoretical approaches, and inspire young, talented researchers to pursue further development. The Fluid Mechanics Conference (FMC2024) took place from September 10–13, 2024, at the Warsaw University of Technology, Poland. The conference welcomed approximately 100 participants from universities and institutions across Poland, the Czech Republic, Ukraine, France, the UK, Germany, Israel, India, China, South Korea, and Japan, with a total of 92 submitted abstracts. The program featured twelve regular sessions and six plenary sessions, with distinguished experts in numerical and experimental fluid mechanics delivering plenary talks: • Prof. Krzysztof J. Fidkowski, University of Michigan, Ann Arbor, USA • Prof. Jerzy M. Floryan, University of Western Ontario, London, Canada • Prof. Genta Kawahara, Osaka University, Japan • Prof. Luis P. Ruiz Calavera, Airbus & Universidad Politécnica de Madrid, Spain • Prof. Spencer Sherwin, Imperial College London, UK • Prof. Daniele Simoni, Università degli Studi di Genova, Italy. List of Acknowledgment, Sponsors, FMC2024 Organizing Committee, Honorary Patronage, Sponsors, XXVI FMC 2024 International Scientific Committee, Organizing Committee, Members of the Organising Committee, Volunteers helping during FMC2024 and Reviewers of JP:CS papers are available in this Pdf.
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 | 0.002 | 0.009 |
| 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 | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.595 | 0.423 |
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".