Preface to the Special Issue on: “27th International Conference on Parallel Computational Fluid Dynamics”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The international Conference on Parallel Computational Fluid Dynamics (CFD) is an annual event devoted to the discussion of the latest advances and challenges in the field of high-performance computing for fluid dynamics problems and related multidisciplinary applications. The 27th edition of the conference took place in May 2015 in Montreal, Canada, and was hosted by the CFD Laboratory of the McGill University. The Conference had seven plenary speakers, three parallel sessions and two mini symposia, entitled ‘Enabling Large-Scale Multi-physics Simulations’ and ‘CFD Applications on GPU and Many-Core Architecture’, respectively. A total of 88 research works were presented during the Conference, for a total of 110 participants from both Academia and Industry. Two-thirds of the presentations discussed themes in the area of parallel algorithms and software, GPU computing and peta-scale applications. The remaining one-third addressed the application of parallel solvers to problems in the areas of mechanical and biomedical engineering, turbulent flows, combustion and multi-physics problems. This Special Issue collects a selection of the works presented at the conference. The conference was successful in delivering high-quality talks in terms of scientific relevance and in proposing approaches to address the most modern and (possibly) future demands in the area of high-performance computing and computational engineering.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it