Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Silico methods to predict toxicity have become increasingly important recently, particularly in light of European legislation such as REACH and the Cosmetics Regulation. They are also being used extensively worldwide e.g. in the USA, Canada, Japan and Australia. In assessing the risk that a chemical may pose to human health or to the environment, focus is now being directed towards exploitation of in silico methods to replace in vivo or in vitro techniques. A prediction of potential toxicity requires several stages: 1) Collation and organisation of data available for the compound, or if this is not available, information for related compounds. 2) An assessment of the quality of the data. 3) Generation of additional information about the compound using computational techniques at various levels of complexity - calculation of physico-chemical properties, 2-D, 3-D / MO descriptors and specific receptor modelling / interaction. 4) Use of an appropriate strategy to predict toxicity - ie a statistically valid method which makes best use of all available information (mechanism of action, activity for related compounds, extrapolation across species and endpoints, likely exposure scenario amounts over time etc). 5) Consideration then needs to be given to how this information is used in the real world ie use of expert systems / tools as relevant to assessors (if sufficiently different to previous) - weight of evidence approaches. 6) Finally evidence should be presented from case studies within this area. No other publication brings together information on all of these areas in one book and this publication is unique in that it provides a logical progression through every one of these key stages and defines the use of computational approaches to predict the environmental toxicity and human health effects of organic chemicals. The volume is aimed at the developers and users of in silico toxicology and provides an analysis of all aspects required for in silico prediction of toxicology, including data collation, quality assessment and computational approaches. The contributions from recognised leaders in each of these areas include evidence of the use and applicability of approaches using real world case studies concerning both environmental and human health effects. The book provides a very useful single source reference for people working in this area including academics, professionals, under- and post-graduate students as well as Governmental Regulatory Scientists involved in chemical risk assessment and REACH.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it