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Record W3201184591 · doi:10.1139/bcb-2021-0340

Emerging regulatory challenges of next-generation synthetic biology

2021· article· en· W3201184591 on OpenAlexaffvenue
Taylor Sheahan, Hans‐Joachim Wieden

Bibliographic record

VenueBiochemistry and Cell Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBiosafetyBiosecurityCoronavirus disease 2019 (COVID-19)PreparednessSynthetic biologyPandemicProcess (computing)2019-20 coronavirus outbreakBiotechnologyBusinessBiologyPolitical scienceRisk analysis (engineering)Computer scienceComputational biologyEcologyVirologyLawInfectious disease (medical specialty)MedicineOutbreak

Abstract

fetched live from OpenAlex

Cell-free synthetic biology is a rapidly developing biotechnology with the potential to solve the world's biggest problems; however, this promise also has implications for global biosecurity and biosafety. Given the current situation of COVID-19 and its economic impact, capitalizing on the potential of cell-free synthetic biology from an economic, biosafety, and biosecurity perspective contributes to our preparedness for the next pandemic, and urges the development of appropriate policies and regulations, together with the necessary mitigation technologies. Proactive involvement from scientists is necessary to avoid misconceptions and assist in the policymaking process.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.622

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 head0.015
GPT teacher head0.273
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
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

Citations11
Published2021
Admission routes2
Has abstractyes

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