Papers of the ICECE-2021 Conference held in Chengdu, China
Bibliographic record
Abstract
One of the main objectives of the International Congress on Energy Chemistry and Engineering 2021 (ICECE-2021) was to provide a forum for the discussion of a wide range of topics in modern energy conversion, catalysis, energy storage, nanochemistry, photochemistry and electrochemistry, focusing on modelling, theory, experimentation, and applications. ICECE-2021 was held between June 18th and June 21st, 2021 in the historic City of Chengdu, China. The conference attracted more than 500 participants and over 200 speakers, with China, Germany, Canada, USA, Turkey, and Japan representing and heading the list of papers presented at the meeting. The topics covered in ICECE-2021 included energy conversion and catalysis; energy storage and materials; energy, photochemistry and electrochemistry; energy nanochemistry and nanotechnology; hydrogen energy and fuel cells; solid-state batteries; and lithium batteries. Twelve early career scientists delivered talks at the Young Scientists Forum, while eight more young authors contributed papers to the conference proceedings. I was the Honorary Chair of ICECE-2021 and I delivered a welcome speech to the audience, who attended the conference virtually via an online system because of the COVID-19 global pandemic. List of Organizing Committee List are available in this pdf.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.034 |
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".