Bibliographic record
Abstract
With the increasing global energy and environmental issues, the sustainable development of renewable energy and ecosystems has become a hot topic and an important issue worldwide.2023 International Conference on Renewable Energy and Ecosystem (ICREE 2023), held in Beijing, China from July 28 to 30, 2023, aims to promote research and development in the field of renewable energy and ecosystems, bringing together scholars, engineers, and industry experts from different countries and regions across various disciplines, including environmental science, engineering, ecology, energy science, and more.The scope of ICREE 2023 includes but are not limited to the development, utilization, and management of renewable energy such as solar, wind, biomass, geothermal, and ocean energy, the protection, restoration, and sustainable development of ecosystems, environmental pollution control, waste disposal, and resource recovery, as well as relevant policies, economic and social factorsThe conference has 4 keynote speeches and 3 invited speeches in total, and it has drawn about 160 delegates from 9 countries (China, India, Canada, UK, India, Singapore, Malaysia, Thailand, South Africa).The conference comprised a diverse spectrum of highly technical presentations by keynote and invited speaker sessions and authors of submitted papers.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.514 | 0.356 |
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".