Bibliographic record
Abstract
The capability to relate phenotypic effects to damage associated with either the mitochondrial or nuclear genome is especially useful under a number of circumstances. Potential hazardous exposures can be evaluated for genotoxicity and related to diseases, particularly cancer. The correlation of DNA damage with adverse health effects is also important in evaluating the safety of various chemical agents and prospective therapeutics. Many techniques exist that afford the ability to identify and measure cellular DNA damage upon exposure to a suspected genotoxic agent; however, quite often these techniques are limited either by the advanced instrumentation and skill needed to perform the analyses or the amount of time needed and limited information obtained regarding the types of DNA damage generated. Recent advances in cellular-based methods have resulted in the timely and straightforward collection of reliable and specific data regarding levels of damage and the identity of the damage products. Antibodies developed for DNA damage lesions allow for the direct measurement of those lesions within a population of exposed cells, while the automation of the single-cell gel electrophoresis (comet) assay and the use of scoring software have led to rapid and standardized data collection. This essay describes the usefulness of these approaches, while providing a brief experimental overview of the techniques.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".