Bibliographic record
Abstract
Affected by COVID-19, the conference committees decided to cancel onsite interactive of 2020 International Conference on Advanced Electrical and Energy Systems (AEES2020) in Osaka, Japan, and change the conference form to full remote conference in a virtual environment from August 18-21, 2020 by “ZOOM”. In order to insure all accepted papers can be published on time successfully, this conference cannot be postponed or cancelled. So virtual conference is the great option for conference organizer, committee members and participants. This year, there are around 14 participants including Keynote experts from Japan, China, Canada, Astralia, UAE, France, Italy and Sultanate of Oman to attend AEES2020 virtual conference. All keynote, parallel oral sessions, discussions and other activities have been offered in online service. Each expert has 45 mins for key speech while each author only has 15 mins for oral presentation including “question and anwers” part. Last but not the least, we’d like to express our gratitude to thank all members of conference committee members for their dedication and contribution to the conference; without their hard work, the conference would not be successful.
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 | 0.007 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.455 | 0.303 |
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".