Bibliographic record
Abstract
Following the success of 2014, 2016, 2018, and 2021 International Conference on Science & Technology Applications in Climate Change (STACLIM), the Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia (UKM) is proud to extend our promotion of research and education for the advancement of climate change studies. The 2022 International Conference on Science & Technology Applications in Climate Change (STACLIM 2022) with the theme “Climate change mitigation action through the lens of science and technology” is the fifth in the series of conferences organized by IPI. This year the conference was carried out in virtual form through the Webex platform (29 – 30 November 2022) due to the COVID-19 travel restriction. Through the virtual form, the science community is able to share their research findings in time. The aim of this conference is to bring together researchers in fields of Environmental Science, Health Sustainability, Mathematics, Sustainable Energy, Economic Sustainability, Socio-Cultural Studies, Social Science, Atmospheric Science, and related fields, to present their research findings as well as create new opportunities for future research collaborations. This event is envisaged to witness active participation from various eminent environmental and earth scientists, engineers and students from academia, industry and government sectors for addressing complications associated with climate change and to draw forth novel and ground-breaking initiatives and solutions for climate resilience. The plenary sessions in the main room were opened by two keynote speeches from leading experts including Prof. Dr. Lisa Stein from University of Alberta, Canada on “Microbial Solutions to Mitigating Climate Change”, Prof. Dr. Haruko Kurihara from University of Ryukyus, Japan, on “Ocean acidification impacts on marine ecosystem and its potential mitigation solutions”. As the keynote session was open for public registration, we had participants joining the event. It was then followed by the invited speaker sessions consisting of Prof. Dr. Fredolin Tangang (UKM), Assoc. Prof. Dr. Rawshan Ara Begum (Macquarie University, Australia), Dr. Shantanu Kumar Pani (National Central University, Taiwan) and Mr. Saud Aldrees (University of Oxford, England). The program was then continued with oral presentation of 72 papers in 3 parallel breakout rooms. Each presenter was given up to 15 mins for presentation and Q&A sessions. There were additional 13 non-presenters who joined in during the presentation session. Presenters and participants have attended the conference from their respective countries including Malaysia, Indonesia, Philippines, USA, China, Saudi Arabia, Iraq, Algeria, India, and Ukraine. The conference went well with great support and synergy of the staff and personnel from Institute of Climate Change, UKM. To document and promulgate the research findings and ideas shared, we are very pleased to publish the accepted research papers of STACLIM 2022 in IOP Conference Series: Earth and Environmental Science (EES). The Editors List of Organizing Committee is available in this Pdf.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.563 | 0.390 |
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".