The Online Conference of the Tenth International Conference and Workshop on High Dimensional Data Analysis (ICW-HDDA-X) 2020
Bibliographic record
Abstract
B N Ruchjana1, J Rejito1, A Pradana1, A N Falah1, M Alawiyah1, R C I Prahmana2, F C Permana3 and K A Sugeng4 1Department of Mathematics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia 2Department of Mathematics Education, Universitas Ahmad Dahlan, Jl. Pramuka 42, Umbulharjo, Yogyakarta, Indonesia 3Department of Multimedia Education, Universitas Pendidikan Indonesia, Indonesia, Jl. Dr. Setiabudi No.229, Isola, Bandung, Jawa Barat 40154, Indonesia 4Department of Mathematics, Universitas Indonesia, Jl. Margonda Raya, Depok, Jawa Barat 16424, Indonesia E-mail: budi.nurani@unpad.ac.id Preface The Tenth International Conference and Workshop on High Dimensional Data Analysis (ICW-HDDA-X) is a join conference between institution in Indonesia and Canada. It is initiated since 2018 by collaboration between Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran and Faculty of Mathematics and Science, Brock University, Canada. The planning of the ICW-HDDA-X 2020 was an offline conference and it will be held at the Prime Plaza Hotel, Sanur-Bali, Indonesia on 12-15 October 2020. Many expert people gave a commitment to come to Bali Indonesia for academic and also for tourism. Because of there is a pandemic Covid-19, so the ICW-HDDA-X 2020 changed to be Online Conference. The ICW-HDDA-X is still organized and hosted in Bandung, Indonesia and the schedule is shorted to be 13-14 October 2020 due to the different time zone between Indonesia and Canada.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.075 |
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".