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Record W3010798611 · doi:10.1002/mc.23189

Contaminated and misidentified cell lines commonly use in cancer research

2020· article· en· W3010798611 on OpenAlexfundno aff
ST Cheung, Stephen L. Chan, Kwok Wai Lo

Bibliographic record

VenueMolecular Carcinogenesis · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
FundersHealth and Medical Research FundTerry Fox Foundation
KeywordsBiologyTranslational researchCell cultureCancer cell linesMedical researchBasic researchCancerCredibilityComputational biologyDiseaseCellBioinformaticsCancer researchCancer cellBiotechnologyComputer sciencePathologyGeneticsMedicine

Abstract

fetched live from OpenAlex

Cell line authentication is important for credibility concern and scientific reproducibility. Authenticated cancer cell lines retain the properties of the cancers of origin and serve valuable resources for medical research. Experimental results commonly will be validated in more than one cell line to avoid specific cell line effect not generalizable to the disease on the whole. The use of appropriate and verified cell lines would therefore be very important in preclinical studies of translational research, bridging basic research to clinics.

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)0.021
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.012

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 head0.080
GPT teacher head0.344
Teacher spread0.264 · 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.

Study designObservational
DomainMethods
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

Citations9
Published2020
Admission routes1
Has abstractyes

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