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Record W2342828304 · doi:10.1039/9781847559753

Concepts in Toxicology

2009· book· en· W2342828304 on OpenAlexaff
John H. Duffus, Douglas M. Templeton, Monica Nordberg

Bibliographic record

Venuenot available
Typebook
Languageen
FieldChemistry
TopicHistory and advancements in chemistry
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsToxicologyBiology

Abstract

fetched live from OpenAlex

Toxicology has never been more important. Advances in chemistry and technology offering improvements in the quality of human life become ever more rapid, bringing with them the potential for new toxicity hazards. This has led to legislation requiring toxicity testing and risk assessment for all chemicals and their uses. The new REACH (Risk Evaluation and Authorization of Chemicals) Regulation has profound economic consequences because, without official authorization, a chemical cannot be marketed. This book explains, in depth, the ideas underlying current advances in toxicology and its application in regulating and ensuring the safe use of chemicals. Sometimes old ideas have become assumptions that have become embedded in related laws and regulation, even though the thinking of toxicologists has moved on in line with developments in science. This leads to confusion in public understanding that the book should dispel. There are also fundamental ideas in toxicology that are not well understood concerning the concepts of hazard and risk and even about what constitutes a chemical. For many people the word 'chemical' describes manmade substances only. In fact, it is correctly applied to all substances that exist, from pure elements to the most complex biological molecules in food and medicines. This is further complicated by the complex distinction between the descriptors, 'toxic' and 'nontoxic'. Developments in epigenetics are revolutionizing our understanding of mutagenicity and carcinogenicity. Improved understanding of apoptosis and necrosis leads to improved interpretation of potentially toxic effects at the cellular level. The recently defined term 'chemical speciation' is driving more targeted research on the toxicity of inorganic chemicals. This book explains the concepts implied by key toxicological terms using diagrams to illustrate the relationships between them. It is an essential aid to understanding the new demands from regulators of risk assessment and to the implementation of appropriate risk management.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0030.014
Scholarly communication0.0090.009
Open science0.0040.006
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0300.023

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.

Opus teacher head0.014
GPT teacher head0.289
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations12
Published2009
Admission routes1
Has abstractyes

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