Bibliographic record
Abstract
both of whom have industrial catalytic experience (Avantium, Shell, Akzo-Nobel and Albermale).One is a chemist experienced in parallel catalyst screening and the other is a chemical engineer, experienced with large-scale feedstock processing, including the respective biomass processes.Obviously, this is a good combination.The Preface is written by the CTO of Royal Dutch Shell, Jan van der Eijk, on the Next Feedstock Transition which shows the general focus of the book and shows the reader that this is a very relevant topic for global players and society in general.The editors have chosen authors or teams of authors for the single book chapters and the selection is a nice mixture between academia, public organizations and industry, involving on the public organization and industry side, authors from Statoil, LLC, Amyris, Baskem, Johnson Matthey, Lignol Innovations, and diverse US National Departments.Most of the writers come from North America (US, Canada), South America (Brazil) and Europe with a focus on the Netherlands.No authors from Asia or Australia are involved.Yet, the book can be considered to aim at a condensed 'world view'.A first look at the content list shows that the chapters are well structured and that the reader gets much information.Since the whole biomass topic comprises a myriad of information and subtopics, we expected such massive load of information; basically this is a new chemistry that shares all the facets of the existing one.It is certainly a hard job to condense that into one book and the authors have succeeded here.The next question then is this biomass book different from all the many, many others (especially those of the good Wiley-VCH series) mentioned as "Related Titles" in the beginning of the book.I have seen only a few of them, which limits my judgment here.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".