Bibliographic record
Abstract
The Best Paper Award scheme was established in the journal in 1985. The purpose of the scheme is to recognize prominent papers, promote high-quality research published in the journal, and provide encouragement and acknowledgment of authors' hard work. From the articles that have been nominated for the award this year, I am delighted to announce that the chosen paper is entitled “Microsecond nonlinear model predictive control for DC-DC converters” by Aleksandra Lekić, Ben Hermans, Nenad Jovičić, and Panagiotis Patrinos (https://doi.org/10.1002/cta.2737). The paper proposed a novel nonlinear model for the real-time predictive control in DC-DC converters. The controller was implemented using the PANOC solver and was efficiently applied to different converter topologies in the microsecond range. The theory was complemented by several test cases and by experimental validation. I thank the authors of all nominated articles wishing them success in the future and many congratulations to the winners of this year's award. Special thanks go to Dr. Paolo Manfredi (Politecnico di Torino, Italy), member of the Editorial board for overseeing the nominations and the selection process.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.153 | 0.166 |
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".