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Record W1887357633 · doi:10.1002/9780470744307.gat009

Evaluation of Toxicological Interactions for the Dose‐Response Assessment of Chemical Mixtures

2009· other· en· W1887357633 on OpenAlexaff
Kannan Krishnan, Sastry Isukapalli, Jonathan Boyd

Bibliographic record

VenueGeneral, Applied and Systems Toxicology · 2009
Typeother
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhysiologically based pharmacokinetic modellingWeightingInternal doseBiological systemBiochemical engineeringChemistryComputer sciencePharmacologyPharmacokineticsMedicineEngineeringBiologyRadiochemistry

Abstract

fetched live from OpenAlex

Abstract Dose‐response assessment for chemical mixtures involves the characterization of the relationship between administered dose (or more appropriately target tissue dose) and tissue response, in order to facilitate the determination of safe exposure levels for humans. When interactions among chemicals occur, the consideration of mechanisms would be necessary for the conduct of scientifically sound dose‐response assessment for mixtures. The present chapter focusses on the current approaches for evaluating toxicological interactions for the dose‐response assessment of chemical mixtures. The approaches described in this chapter include: (i) interaction matrix method, (ii) interaction weighting ratio method and (iii) physiologically based pharmacokinetic (PBPK) modelling. The unique use of PBPK models in predicting the change in tissue dose of mixture components as a function of dose, route, exposure scenario and mixture complexity is highlighted. Finally, the interaction‐based dose‐response analysis of chemical mixtures is described, along with illustrative examples.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.391
Teacher spread0.359 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2009
Admission routes1
Has abstractyes

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