Sampling Research on Advanced Computational Intelligence in Canada
Bibliographic record
Abstract
The 1999 IEEE Canadian Conference on Electrical and computer Engineering (CCECE'99) was held from May 9 to 12, 1999, at the Shaw Conference Centre in Edmonton. The conference was a great success with over 380 papers presented and more than 400 peoples from 38 different countries presenting their recent research results. The area of Computational Intelligence was one of the vivid pursuits presented at the conference. Subsequently, we have been invited by the Editors-in-Chief of the Journal of Advanced Computational Intelligence to prepare a Special Issue of the Journal CCECE'99 conference. After a careful and strict peer review process, we have chosen six papers to be included in this special issue. They are selected from more than 20 papers submitted to this special issue, which are extended versions of the papers presented at the CCECE'99 conference in the areas of advanced computational intelligence. The papers fully reflect the breadth and diversity of conceptual and algorithmic facets of Computational Intelligence along with a spectrum of applications. We thank the authors and reviewers for doing an excellent job. We are grateful to Kaoru Hirota and Toshio Fukuda for making this selection of papers a part of the journal. We do hope the readers will enjoy this 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.027 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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".