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Record W4401052174 · doi:10.1007/978-3-031-63917-3

International Regulation of Gene Editing Technologies in Crops

2024· book· en· W4401052174 on OpenAlexfundno aff
Lisa F. Clark, Jill E. Hobbs

Bibliographic record

VenueSpringerBriefs in environmental science · 2024
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersSaskatchewan Wheat Development CommissionGenome PrairieNational Academies of Sciences, Engineering, and MedicineAlberta Wheat CommissionCanadian Food Inspection AgencyPhilippine Rice Research InstituteGenome CanadaEuropean Food Safety AuthorityWestern Grains Research FoundationWorld Health OrganizationEuropean CommissionMinistry of Agriculture - SaskatchewanAgriculture and Agri-Food CanadaNational Science Foundation
KeywordsGenome editingBiologyGeneBiotechnologyComputer scienceGeneticsCRISPR

Abstract

fetched live from OpenAlex

SpringerBriefs in Environmental Science present concise summaries of cuttingedge research and practical applications across a wide spectrum of environmental fields, with fast turnaround time to publication.Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic.Monographs of new material are considered for the SpringerBriefs in Environmental Science series.Typical topics might include: a timely report of state-of-the-art analytical techniques, a bridge between new research results, as published in journal articles and a contextual literature review, a snapshot of a hot or emerging topic, an in-depth case study or technical example, a presentation of core concepts that students must understand in order to make independent contributions, best practices or protocols to be followed, a series of short case studies/debates highlighting a specific angle.SpringerBriefs in Environmental Science allow authors to present their ideas and readers to absorb them with minimal time investment.

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 score0.224
Threshold uncertainty score0.607

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 head0.004
GPT teacher head0.242
Teacher spread0.238 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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