Bibliographic record
Abstract
Copyright: © 2012 Eskin NAM. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Oxidative stress is involved in the development of many chronic diseases including cancer. It has been defined as an imbalance between the level of pro-oxidants (reactive oxygen species, ROS) produced during normal metabolism and the organism’s endogenous antioxidant defence system. The role of various natural antioxidant defence systems to minimize oxidative damage caused by these free radicals was established using animal models in which these defence systems were knocked out [1]. This resulted in the promotion of cancer which was attributed to DNA damage by the formation of 8-hydroxy2-deoxyguanosine (8OHdg). Consequently, a concerted effort has been made to establish the efficacy of such traditional antioxidants as vitamins A, E and C, as well as a search for new antioxidants to minimize such damage. Over 200 epidemiological studies strongly associated low consumption of fruits and vegetables with the incidence of cancer suggesting that antioxidants might be a solution [2]. Consequently, should cancer patients be encouraged to take multivitamin supplements as part of their therapy?
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".