Bibliographic record
Abstract
Overall, the book is very well done.A notable feature is the various papers that have been prepared on different scaling studies.The references cited within this book represent appli-amount of referencing; 100 references per chapter was not uncommon.The book is essential for anyone dealing with new cations from a variety of locations and studies and the authors are to be commended for their incorporation of several exam-or unusual compounds.I suspect most consultants and regulators in this area will need to have a copy, if not to use them-ples.The outline of the book provides an introduction about the problem of scaling and the general principles of upscaling selves, then to aid in interpretation of what others are using.The book may also serve as an advanced reference book for (aggregation) or downscaling (disaggregation).All of the major terms that the readers need to understand in order to put students, although I doubt there are many courses offered that could use this as a text.If there were, it could serve that concepts into practice are defined in a glossary and there are ample figures to visually explain the concepts.The material in purpose well.The indexing is good.I found few errors.I think this book will be in demand.-STEVESHEPPARD, the book is presented in an instructive manner that leads the reader through theory into practice.There are only three ECOMatters Inc., Pinawa, MB, Canada, R0E 1L0 (sheppards
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".