Ecological, biomedical and epidemiological approaches to understanding oxidative balance and ageing: what they can teach each other
Bibliographic record
Abstract
Summary 1. Oxidative stress and antioxidants have been studied in a number of disciplines, but these disciplines have not always been informed by each other’s work. 2. Here, we discuss the strengths and weaknesses of oxidative stress and antioxidant research in the areas of (i) ecology, (ii) ageing research, (iii) epidemiology, and (iv) physiology of model organisms, with an emphasis on what ecologists can learn from and bring to other fields. 3. We find that physiologists provide an essential role in clarifying basic mechanisms, but that many of their findings are context‐dependent. Ecologists and epidemiologists bring strengths in understanding the relevance of context, whether it is across species, environments, or diets. Ageing research has helped to provide a clear theoretical framework for all fields and has thus spurred much of the research to date. 4. Comprehensive understanding of the complexity of oxidative balance systems will rely on integration of knowledge of physiological pathways from studies of model organisms, knowledge of long‐term interactions of many parameters from epidemiological studies, and knowledge of specificity and generality of results across species and conditions as gleaned from ecological studies. 5. Studies of ageing have helped to show that all fields of antioxidant/oxidative stress research should focus not on individual markers of oxidative damage or antioxidant status, but on how they integrate into oxidative balance systems. Free radicals can have beneficial roles in signalling as well as causing damage and should not be interpreted out of context.
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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.027 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".