Bibliographic record
Abstract
International Conference on Advanced Electrical and Energy Systems (AEES 2020) were held online during August 18-21, 2020. These conferences addressed diverse topics related to recent trends and progress made in the field of advanced energy systems. Advanced electrical and energy systems have been the main topics in research and development at the academic, industry and business level in recent year. It enables renewable generation, distributed generation, energy storage, transmission, distribution and demand management. The benefits of advanced electrical and energy systems include the enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality. However, all these prospective transformations also bring with them numerous challenges and opportunities. Concerns relating to global warming, caused by green house gas (GHG) emissions from various sources, have raised general awareness among the governments and the public of the need to produce energy, in particular energy in the electrical form, from renewable energy sources that do not produce GHGs. The papers presented at these conferences and included in these Proceedings cover a wide variety of topics on various aspects of advanced energy sources of the future, the focus of these series of conferences held in August 2020. We hope that they will offer the readers a good opportunity to explore the state-of-the art developments and future directions of research on these topics. On behalf of Conference Committees Prof. Om P. Malik, University of Calgary, Canada (LFIEEE)
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.377 |
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".