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Record W2099698180 · doi:10.1101/pdb.prot080192

Induction of Germline Apoptosis in <i>Caenorhabditis elegans</i>

2014· article· en· W2099698180 on OpenAlexaff
Benjamin Lant, W. Brent Derry

Bibliographic record

VenueCold Spring Harbor Protocols · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsCaenorhabditis elegansGene knockdownRNA interferenceGermlineApoptosisCell biologyBiologyGeneRNAModel organismGeneticsDNAChemistry

Abstract

fetched live from OpenAlex

RNA interference (RNAi) is an incredibly powerful tool for rapid and efficient knockdown of gene expression. This technology can be used to induce apoptosis in the germline of Caenorhabditis elegans. Genotoxic stressors such as ionizing radiation (IR), ultraviolet light, chemical mutagens (e.g., N-ethyl-N-nitrosourea [ENU]), and DNA cross-linking reagents can also be used to stimulate apoptosis. These approaches, described here, combined with the powers of in vivo imaging methods, should keep C. elegans apoptosis researchers busy for several years, sorting out how various signaling pathways influence life and death decisions in this organism.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.235 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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