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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 OpenAlexaff
Elisabeth A. Marnik, Carla Bautista, Anna Drangowska-Way, Caitlin M. A. Simopoulos, Thomas Merritt

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.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0100.006

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

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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2021
Admission routes1
Has abstractyes

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