MétaCan
Menu
← Back to cohort
Record W4385575270 · doi:10.4324/9781003382102-10

Biosafety and biosecurity concerns associated with plant genome editing

2023· book-chapter· en· W4385575270 on OpenAlexaboutno aff
Rama Krishna Satyaraj Guru, Ashutosh Sawarkar Ganpatrao, Atul Pradhan Madhao, Rojalin Pradhan, Ayesha Mohanty, P. Panigrahi, R. K. Tarai, Bushra Khatoon

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsBiosecurityBiosafetyBiologyBiotechnologyEcology

Abstract

fetched live from OpenAlex

Biosafety is the field of work and study concerned with controlling biological risks in order to protect laboratory personnel, the local community, and the environment from accidental exposure and infection. Modifications (insertions, deletions, and substitutions) in a living organism&s;s genome are referred to as genome engineering, genome editing, and gene editing. CRISPR-Cas9 is the most extensively used genome editing method, but there are obstacles to overcome when using it to produce upgraded biological weapons with new phenotypes. Plant genome editing is still fraught with methodological, biosafety, and social problems, such as target gene site selection, guide RNA design, off-target effects, and delivery technique. The biggest issue with genome editing is the likelihood of off-target mutations in plants generating unwanted genetic changes. Pre-assembled CRISPR-Cas9 ribonucleoproteins can be administered in vitro to circumvent this. The regulation of genome-edited plants is governed by two frameworks: some governments prohibit the procedure, while others control the properties of the end product. Two obstacles in regulating plant genome editing are gaining market access and addressing social concerns about its biological safety. In certain circumstances, the US Department of Agriculture found that genome editing is equivalent to conventional breeding, but Canada has adhered to the scientific criteria set in its domestic regulatory system. Argentina has established a functioning regulatory mechanism for the approval of genome-edited goods. Non-transgenic goods are those that were created without the use of transgene technology.

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)0.013
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicCRISPR and Genetic Engineering→French-language works237,207→