Bibliographic record
Abstract
The 2023 3 rd New Energy and Energy Storage System Control Summit Forum (NEESSC 2023) took place in Mianyang, China on 26 th to 28 th September 2023 (hybrid form).The aim of the Conference was to provide a platform for the exploration of both fundamental topics and new applications of research fields related to new energy and energy storage, bring different scientific communities together, and facilitate the contacts between science, technology and industry.Over 200 scholars from renowned institutions and universities worldwide, including UK, Denmark, Canada, and China, etc., attended the Conference.They convened to discuss the latest research findings and trends in fields related to new energy and energy storage, fostering international academic exchange and collaboration.It was a highly informative and globally oriented-academic event.The Conference agenda included 14 keynote speeches, as well as plenty of oral and poster presentations.Among the keynote speakers, Professor Hui Pang from Xi'an University of Science and Technology performed a dramatic speech on Advanced Battery Management System-Research on State Estimation and Temperature Monitoring Methods under Electrochemical Mechanism Modeling.According to the actual application requirements, he took the power battery as the research object, combining relevant experimental data and the Multiphysics simulation platform, and carried out the estimation of battery state of charge (SOC) and heat generation rate (HGR) of EVs based on the mechanism reconstruction model.And Professor Shunli Wang from Southwest University of Science and Technology, China shared with us his research on Intelligent Energy Storage -Battery Performance Testing and State Monitoring in New Energy & Energy Storage System.Starting from battery testing methods, he selected typical test data for analysis, and discussed battery condition monitoring strategies based on his team's research progress, so as to contribute to the development of smart energy storage.This Proceedings gathers papers from regular contributions of NEESSC 2023; each contribution submitted for
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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 | 0.009 |
| 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) | 0.553 | 0.384 |
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".