Bibliographic record
Abstract
This book contains a series of exercises and problems posed in the subject of green metrics. Essentially it is a "how to" book on evaluating the material efficiency, environmental impact, safety-hazard impact, and energy efficiency of any kind of chemical reaction or synthesis plan. Only the essential green metrics in each of these categories are used. The introduction highlights the hierarchy of metrics used throughout the book, explains the structure of how the book is arranged, how the problems are posed, and how the reader is to use the book. Examples refer to themes according to the headings given in the table of contents and are arranged in a hierarchical order. Key Features: The topics cover fundamentals in chemistry and the chemical industry in a blended fashion A unique text covering the fundamentals of green metrics from materials efficiency and environmental and safety-hazard impact, to new green technologies and more The book will be useful in a range of chemistry courses, from early undergraduate to advanced graduate courses, whether based in lectures, tutorials or laboratory experiments Using an extensive glossary of terms used in green metrics, each chapter has a specified theme where the relevant metrics definitions pertaining to that theme will be given with one or two illustrative worked examples Supplemental web-based downloadable material including extra problems, full solutions, Excel files, ChemDraw files, templates, and exercises
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.124 | 0.075 |
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".