Surface reactivity measurements as required for grouping and read-across: An advanced FRAS protocol
Bibliographic record
Abstract
Oxidative stress is a widely accepted paradigm associated with different adverse outcomes of particulate matter, including nanomaterials. It has frequently been identified in in vitro and in vivo studies and different assays have been developed for this purpose. Here we describe a newly developed multi-dose protocol of the FRAS assay (Ferric Reduction Ability of Serum). The purpose of this SOP is the measurement of the surface reactivity of nanomaterials under physiological conditions. Antioxidative components as present in human blood serum (HBS) serve as reporter molecules. The assay separates the oxidative damage from the read-out of the reporter molecules. The results show significantly enhanced repeatability with better sensitivity towards low reactivity, enabling application of FRAS both to a rough grouping by reactive vs. passive nanomaterials and further to substantiation of read-across by enhanced resolution of the similarity between different nanoforms of the same substance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".