Bibliographic record
Abstract
A dozen chemical reaction engineers from academia and industry recently published a sequel to the “Vision 2020: Reaction Engineering Roadmap,” published in 2001. In this webinar, that team of authors will provide a summary of our recently issued perspective paper, “Vision 2050: Reaction Engineering Roadmap,” available from ACS Engineering Au. This webinar will follow the format of our recent paper, with brief summaries of our perspectives regarding the field of reaction engineering in the context of four industry sectors (basic chemicals, specialty chemicals, pharmaceuticals, and polymers) and five technology areas (reactor system selection, design and scale-up, chemical mechanism development and property estimation, catalysis, nonstandard reactor types, and electrochemical systems). This seminar will feature multiple speakers, a subset of the team of the corresponding paper's authors. The entire coauthor team is as follows: Praveen Bollini (U. of Houston), Moiz Diwan (Abbvie), Pankaj Gautam (SABIC), Ryan Hartman (New York U.), Dan Hickman (Dow), Marty Johnson (Eli Lilly), Moto Kawase (Kyoto U.), Matt Neurock (U. of Minnesota), Gregory Patience (Polytechnique Montréal), Alan Stottlemyer (Dow), Dion Vlachos (U. of Delaware), and Ben Wilhite (Texas A M).
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.062 |
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".