Preface from the IEEE LAEDC 2022 Conference General Chair
Bibliographic record
Abstract
The Latin-America Electron Device Conference (LAEDC) is a premier IEEE Latin-American and Caribbean Electron Devices conference sponsored by IEEE's Electron Device Society (EDS).Its first edition took place in Colombia in 2019.In 2020 Costa Rica hosted the second edition.Because of the COVID pandemic, in 2021 the conference took place as a full virtual edition.This 2022 the conference took place in the City of Puebla in Mexico with a hybrid in-person and virtual mode.Despite the COVID pandemics the conference shows a historic steadily growing throughout the last 4 years of existence, as showed by the 128 submitted contributions from 37 different countries from America, Africa, Asia, and Europe.This progressive growing comes accompanied by a strong participation of 140 authors from Latin America and the Caribbean.After a careful review process 78 papers out of the 128 were accepted for either in-person or virtual presentation, and for publication in the IEEE conference proceedings.With the review process we keep a high quality of the technical contributions with a 61 % acceptance rate.Latin-America and the Caribbean authors contributed with over 50 % of the submitted papers, followed by India with over 13%, USA and Canada with over 11 %, and Europe with over 20 %, in addition to contributions from Saudi Arabia and Australia.The LAEDC conference is accomplishing the objective of spreading and attracting more participants into the electron devices university and research community in Latin America and in the Caribbean region.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.131 | 0.158 |
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".