IUPAC Announces the 2021 Top Ten Emerging Technologies in Chemistry
Bibliographic record
Abstract
Abstract IUPAC has released the results of its 2021 search for the Top Ten Emerging Technologies in Chemistry. The goal of this project is to showcase the transformative value of Chemistry and to inform the general public on the potential of the chemical sciences to foster the well-being of Society and the sustainability of our Planet. Following the same guidance as it did last year, the Jury The Jury was an international group of objective and unbiased experts who reviewed and discussed a pool of nominations, and ultimately selected the final top ten. The following comprised the panel of judges for the 2021 Top Ten Emerging Technologies in Chemistry: Chair, Michael Droescher, (German Association for the Advancement of Science and Medicine), Jorge Alegre-Cebollada (Centro Nacional de Investigaciones Cardiovasculares, Spain), Sophie Carenco (French National Center for Scientific Research, France), Javier García Martínez (Universidad de Alicante, Spain), Ehud Keinan (Technion, Israel), Rai Kookana (CSIRO Land & Water, Australia), Greg Russell (University of Canterbury, New Zealand), Ken Sakai (Kyushu University, Japan), Natalia P. Tarasova (D. I. Mendeleev University of Chemical Technology, Russia), and Bernard West (Life Sciences Ontario, Canada). , a selection of international experts, identified different emerging technologies, scientific advances in between a discovery and a fully-commercialized ideas, with outstanding capacity to open new opportunities in chemistry, sustainability, and beyond. The 2021 finalists are (in alphabetical order):
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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 | 0.066 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.033 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.038 | 0.025 |
| Insufficient payload (model declined to judge) | 0.027 | 0.024 |
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".