Bibliographic record
Abstract
DMCIT 2020 was held in the form of on-line conference on May 21, 2020, which should be held in Xi’an, China from May 21-24, 2020 as planned. Due to the impact of COVID-19, many communities from all over the world were under strict health measures and strict travel restrictions, and participants of 4th International Conference on Data Mining, Communications and Information Technology (DMCIT 2020) which is organized by Asia Pacific Institute of Science and Engineering also meet with the difficulty of travel restrictions. For communities, if our conference was not held in a virtual form, it would add huge work on traffic system, Exit-Entry system, medical system, etc., which is quite a serious issue, to actively respond to the call of the government, to strengthen the protection work, to effectively reduce people gathering and prevent COVID-19, considering the situation that most of the authors would like to publish their articles and make academic communications as scheduled, DMCIT 2020 was held on-line instead of postponing the conference. There are 92 people attended this on-line conference, who are from Indonesia, Republic of Korea, Thailand, Canada, Saudi Arabia, Australia, Japan, UK, USA, Nigeria, China and more countries and districts. The conference provided a useful and wide platform both for display the latest research and for exchange of research results and thoughts in Data Mining, Communications and Information Technology and other topics. The participants of the conference were from almost every part of the world, with background of either academia or industry, even well-known enterprise. The success and prosperity of the conference is reflected high level of the papers received.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.571 | 0.434 |
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".