Bibliographic record
Abstract
Abstract In the past few years, rapid changes in many aspects of living happened globally. The issues of industrial revolution 4.0, global warming, and the Covid-19 pandemic must be challenged by the people of the world. Citizens in every country have to cope-up with those changes and strive into new normal routines. Higher education institutions hold the duty to anticipate that challenge. The accomplishment of the 1st Webinar International Conference on Mathematics, Natural Sciences and Education in the New Normal Era, which was held Online on 15th October 2020 by FMIPA Universitas Negeri Manado-Indonesia, attended virtually by more than 300 participants of lecturers, researchers, teachers, graduate and undergraduate students from 12 universities across the country. Involved in 6 keynote speaker presentation; 4 foreign universities (Japan, Canada, Italy, Thailand) and 2 Indonesian. This Webinar also consists of 7 tracks such as: Biology, Physics, Chemistry, Mathematics, Natural Science, Education and STEM (Science, Technology, Engineering and Mathematics). As a continuance of this first Webinar, the Proceedings of 82 submitted abstracts are prepared. Hopefully, this Proceeding will be useful for all of us. After all, I would like to thank to all keynote speakers, presenters, participants, as well as to all parties who supported in the establishment of this Webinar. List of Editors, Committee and Conference Photograph are available in the pdf.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".