Guidance for Derivation of Chemical-Specific Adjustment Factors (CSAF)—Development and Implementation
Bibliographic record
Abstract
This manuscript addresses the content of and considerations in the implementation of guidance in the use of kinetic and dynamic data to inform quantitatively extrapolations for inter-species differences and human variability in dose response assessment developed in a project of the International Programme on Chemical Safety (IPCS) initiative on Harmonisation of Approaches to the Assessment of Risk from Exposure to Chemicals. The guidance has been developed and refined through a series of planning and technical meetings and larger workshops of a broad range of participants from academia, government agencies and the private sector. The guidance for adequacy of data for replacement of defaults for interspecies differences and human variability commonly adopted in the derivation of Tolerable or Acceptable Intakes is presented principally through illustrative reference to case examples. The relevant guidance is addressed in the context of several generic categories, including determination of the active chemical species, choice of the appropriate kinetic parameter or endpoint (dynamic component) and nature of experimental data, the latter, which includes reference to the relevance of population, route and dose and the adequacy of the number of subjects/samples
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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 | 0.036 | 0.128 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".