Valeurs toxicologiques de référence - Le dioxyde de titane sous forme nanoparticulaire
Bibliographic record
Abstract
A toxicological reference value, or TRV, is a toxicological index qualifying or quantifying a risk to human health. It establishes the link between exposure to a toxic substance and the occurrence of an adverse health effect. TRV are specific of a duration of exposure (acute, subchronic or chronic) and a route of exposure (oral or inhalation). The construction of TRV differs according to the knowledge or assumptions made about the mechanisms of action of the substances. Currently, the default assumption is to consider a monotonous relationship between exposure, or dose, and effect, or response. Based on current knowledge and by default, it is generally considered that, for non-carcinogenic effects, toxicity is only expressed when a dose threshold is exceeded (Anses, 2017a).In practice, the construction of a TRV involves the following steps:- identify and analyse available toxicity data, based on epidemiological and/or experimentalstudies;- identify the target organ(s) and critical effect;- identify the establishment assumption, with or without a dose threshold, depending on themode of action of the substance;- choose a key study of good quality that allows establishing a dose-response relationship;- define a critical dose in humans or animals from this study, and in the case of a critical doseobtained in animals, adjust this dose to humans;- for a threshold TRV, apply uncertainty factors to this critical dose in order to derive a TRVapplicable to the entire population concerned;- for a TRV without threshold, perform a linear extrapolation at the origin to determine anexcess of unit risk.The development of TRVs follows a highly structured and stringent approach involving collective evaluations by groups of specialists.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".