Bibliographic record
Abstract
2013 marked a wonderful year for IEEE TKDE (Transactions on Knowledge and Data Engineering). While the statistics for November and December 2013 were not available when this editorial was written, TKDE received 822 submissions in the first 10 months of 2013. Among those submissions, 601 received their first round reviews, 20 submissions were invited for minor revision, 150 submissions were invited for major revision, 243 submissions were declined, and 188 were administratively rejected mainly due to topics out of the scope and clear incompetence in technical quality. Final decisions on 458 of those 822 submissions were made, among which 21 were accepted, while there are many other submissions that are still under revision or the second round review. In total, 125 submissions have been accepted in the first 10 months of 2013, including those submitted in 2013 and earlier. The statistics show that TKDE is in a healthy and fruitful state and, at the same time, remains a highly competitive venue for academic publication. I want to thank all authors who submitted to TKDE, and all reviewers and associate editors who helped to run the submission selection process as smoothly as it can be. Your consistent contributions and support make TKDE a fruitful and professional journal. I want to sincerely thank the four associate editors who finished their terms in the second half of 2013: Drs. Elena Ferrari, Wook-Shin Han, Haixun Wang, and Aidong Zhang. Their significant contributions to the quality and reputation of TKDE have benefited many authors, readers, and reviewers. At the same time, I want to officially welcome the six associate editors who joined the editorial board in the second half of 2013: Drs. Eamonn Keogh, Feifei Li, Tao Li, Ee-Peng Lim, Stan Matwin, and Myra Spiliopoulou. Particularly, Eamonn Keogh has been kind enough to rejoin the TKDE editorial board after he retired from his first service several years ago. This group of newly appointed associate editors represents our interest and determination in recruiting the most established and active working experts in the wonderful wide spectrum of knowledge and data engineering. Moreover, they are very committed and dedicated to serving the community and handling the review processes, as testified by their rich experience.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.136 | 0.159 |
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".