Computational science and its applications
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
Computational Science is a main pillar of most of the present research, industrial and commercial activities and plays a unique role in exploiting ICT innovative technologies.Due to the latest development and the availability of high performance computing, including parallel computing, grid computing, and cloud computing, there is a critical need to employ efficient and effective computational methods and algorithms in various applications, including computational biology, computational geometry, computational physics, computation chemistry, computational finance, graphics and visualization, scientific data management, data mining, etc.This issue features selected papers from the International Conference on Computational Science and Its Applications (ICCSA2009) held in June 29-July 1, 2009, Kyung Hee University, Suwon, South Korea.In addition to extended papers from ICCSA2009, a special issue CFP has been distributed to a wider community through various mailing lists.Finally, we selected six papers to be included in this issue.The first paper presents Newton method for nonlinear dynamic systems.It presents numerical algorithms to solve nonlinear equations given the discrete form of the equations.The second and third papers focus on 3D head pose or facial expression, and multi-human tracking.The fourth and fifth papers concentrate on data mining, especially opinion summarization in online customer reviews, and knowledge sharing based ontological approach.Finally, the last paper proposes an entropy optimization for social networks.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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