Bibliographic record
Abstract
The 2023 International Conference on Materials Engineering, New Energy, and Chemistry (MENEC 2023), held in Kuala Lumpur, Malaysia from October 13 to October 15, served as a pivotal forum where researchers and experts from diverse yet interconnected domains converged to exchange research findings.MENEC 2023 was dedicated to advancing collaboration technologies within the realms of materials engineering, new energy, and chemistry, encompassing research and development in academic and industrial sectors.Collaboration technologies encompassed various elements such as theories, methodologies, mechanisms, protocols, software tools, platforms, and services, all of which fostered interaction, coordination, communication, and collaboration among individuals and software and hardware systems. Aims:Providing a platform for researchers, practitioners, and academics to exchange their experiences, ideas, and research discoveries in the fields of materials engineering, new energy, and chemistry.Facilitating discussions on the latest advancements, challenges, and opportunities within these domains.Identifying research gaps, exploring new avenues, and promoting collaborations among researchers, academics, and practitioners.Cultivating interdisciplinary dialogues and advocating for the integration of materials engineering, new energy, and chemistry to address complex problems.The conference featured a total of 3 keynote speeches and 2 invited speeches and attracted approximately 120 delegates from 10 countries, including China, India, Canada, the UK, Singapore, Malaysia, Thailand, South Africa, and Australia.The event encompassed a wide range of highly technical presentations delivered through keynote and invited speaker sessions, as well as by authors of submitted papers.We anticipate that this conference will inspire future research in renewable energy and ecosystems.We eagerly look forward to welcoming all of you to the next MENEC conference.,
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 | 0.002 | 0.002 |
| 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) | 0.576 | 0.417 |
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".