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Record W3209482382 · doi:10.3389/frym.2021.600133

CRISPR: A New Way for Scientists to Edit DNA

2021· article· en· W3209482382 on OpenAlex
Elisabeth A. Marnik, Carla Bautista, Anna Drangowska-Way, Caitlin M. A. Simopoulos, Thomas Merritt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers for Young Minds · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsLaurentian UniversityPROTEOUniversité LavalUniversity of Ottawa
Fundersnot available
KeywordsCRISPRCas9Computational biologyDNABiologyHuman welfareComputer scienceGeneGeneticsPolitical scienceWelfareLaw

Abstract

fetched live from OpenAlex

Just like humans, bacteria can get sick. Some bacteria have a defense system called CRISPR/Cas9 that protects them from infection with viruses. Over the last few years, scientists have adapted this bacterial defense system to be used in the laboratory to alter the DNA of various organisms. This article will explain how CRISPR/Cas9 is used to edit genes and will provide examples of how this technology is useful. Experiments using CRISPR/Cas9 must be carried out ethically, that is, scientists must ensure that all research respects human rights and animal welfare and complies with the law.

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.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.560
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.292
Teacher spread0.285 · 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