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Record W1491658387 · doi:10.1002/0471140856.tx1410s53

Assessment of Metabolic Stability Using the Rainbow Trout (<i>Oncorhynchus mykiss</i>) Liver S9 Fraction

2012· article· en· W1491658387 on OpenAlexaff
Karla Johanning, Gregg A. Hancock, Beate I. Escher, Adebayo Adekola, Mary Jo Bernhard, Christina Cowan‐Ellsberry, Jeanne Y. Domoradzki, Scott D. Dyer, Curtis Eickhoff, Michelle R. Embry, Susan Erhardt, Patrick N. Fitzsimmons, Marlies Halder, James Hill, Dustin D. Holden, Rebecca Johnson, Sibylle Rutishauser, Helmut Segner, Irvin R. Schultz, John W. Nichols

Bibliographic record

VenueCurrent Protocols in Toxicology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsMaxxam (Canada)
FundersEuropean CommissionEuropean Chemical Industry Council
KeywordsRainbow troutTroutFish <Actinopterygii>BiologyCarpFishery

Abstract

fetched live from OpenAlex

Standard protocols are given for assessing metabolic stability in rainbow trout using the liver S9 fraction. These protocols describe the isolation of S9 fractions from trout livers, evaluation of metabolic stability using a substrate depletion approach, and expression of the result as in vivo intrinsic clearance. Additional guidance is provided on the care and handling of test animals, design and interpretation of preliminary studies, and development of analytical methods. Although initially developed to predict metabolism impacts on chemical accumulation by fish, these procedures can be used to support a broad range of scientific and risk assessment activities including evaluation of emerging chemical contaminants and improved interpretation of toxicity testing results. These protocols have been designed for rainbow trout and can be adapted to other species as long as species-specific considerations are modified accordingly (e.g., fish maintenance and incubation mixture temperature). Rainbow trout is a cold-water species. Protocols for other species (e.g., carp, a warm-water species) can be developed based on these procedures as long as the specific considerations are taken into account.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.078
GPT teacher head0.377
Teacher spread0.298 · 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.

Study designObservational
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

Citations65
Published2012
Admission routes1
Has abstractyes

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