Bibliographic record
Abstract
The first point to make about this otherwise well-edited monograph is that the title, "Aging and Age-Related Disorders", could be misleading to those who did not realize that this is one of a series of publications on "Oxidative Stress in Applied Basic Research and Clinical Practice".(The volume number is not given in this book, but the reader is directed to http://www.springer.com/series/8145 for other titles.)Accordingly, all 22 chapters take, as their point of departure, a focus upon the roles of oxidative stress in aging and in various diseases of aging-more specifically, diseases that predominately impact either the cardiovascular or nervous systems.A few other diseases get some attention.For example, diabetes is discussed in many chapters and cataractogenesis is briefly mentioned in the very helpful and basic introductory chapter by Ufuk C ¸akatay, a member of the Faculty of Medicine of Istanbul University.A particularly nice feature of the volume is that one gets "to meet" colleagues from around the world with whom one may have had very little opportunity to know.Although US authors dominate, 11 other nations are represented, including 7 contributions from Japan, 7 from Canada, 6 from Spain, and 5 from Taiwan.Because of my own growing interest in the role of epigenetic drift in the pathogenesis of aging phenotypes, I read the very last chapter first: "An Epigenetic Model for the Susceptibility to Oxidative Damage in the Aging and Alzheimer's Disease", by Nasser H. Zawia and Fernando Cardizo-Pelaez (University of Rhode Island).Motivated by their own research and
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.258 | 0.208 |
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".