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Record W1785550449 · doi:10.1002/0471140856.tx0105s56

The Use of Fish‐Derived Cell Lines for Investigation of Environmental Contaminants: An Update Following OECD's Fish Toxicity Testing Framework No. 171

2013· article· en· W1785550449 on OpenAlexaff
Vivian R. Dayeh, Niels C. Bols, Katrin Tanneberger, Kristin Schirmer, Lucy E. J. Lee

Bibliographic record

VenueCurrent Protocols in Toxicology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsUniversity of the Fraser ValleyUniversity of Waterloo
Fundersnot available
KeywordsFish <Actinopterygii>ToxicityContaminationEnvironmental scienceEnvironmental chemistryFisheryBiologyChemistryEcology

Abstract

fetched live from OpenAlex

Protocols for evaluating chemical toxicity at the cellular level using fish cell lines are described in this unit. Routine methodologies for growing salmonid cell lines, and using them in aquatic toxicology studies that support the mandate of the Organization for Economic Co-operation and Development (OECD) to reduce the use of whole animals in toxicity testing, are presented. Rapid, simple, cost-effective tests evaluating viability of cells with three indicator dyes per sample provides a broad overview of the sensitivity of cells to chemical contaminants. This fluorometric assay involves: (1) alamar blue for metabolic activity, (2) CFDA-AM for membrane integrity, and (3) neutral red for lysosomal function. These protocols are conveniently performed in semi-unison within the same multiwell plates and read at three different wavelengths. Detailed step-by-step descriptions of the assays, parameters to consider, troubleshooting, and guidelines for data interpretation are provided as essential tools for investigating environmental aquatic contaminants at the cellular level.

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)0.009
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.005

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 head0.066
GPT teacher head0.305
Teacher spread0.239 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations63
Published2013
Admission routes1
Has abstractyes

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