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Record W2896443010 · doi:10.1038/nbt.4263

Opening options for material transfer

2018· article· en· W2896443010 on OpenAlexfundno aff
Linda J. Kahl, Jenny Molloy, Nicola J. Patron, Colette Matthewman, Jim Haseloff, David Singh Grewal, Richard A. Johnson, Drew Endy

Bibliographic record

VenueNature Biotechnology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNational Institute of Standards and TechnologyNational Institutes of HealthConsortium of International Agricultural Research CentersPontificia Universidad Católica de ChileKing Mongkut's University of Technology ThonburiUniversiteit GentJohn Innes FoundationQueensland University of TechnologyWoodrow Wilson International Center for ScholarsUniversity of Technology SydneyUniversity College CorkUniversity of TasmaniaCardiff UniversityVanderbilt UniversityLondon School of Economics and Political ScienceBiotechnology and Biological Sciences Research CouncilWorld Health OrganizationWellcome TrustCalifornia Institute of TechnologyKent State UniversityUniversity of DenverSidra MedicineLeona M. and Harry B. Helmsley Charitable TrustGenome Center, University of California, DavisUniversity of California, DavisU.S. Department of EnergySimon Fraser UniversityKing's College LondonUniversity of CreteYale UniversityBill and Melinda Gates FoundationDirectorate for Biological SciencesImperial College LondonPrinceton UniversityMcGill UniversityU.S. Department of CommerceU.S. Department of Health and Human Services
KeywordsTechnology transferGene transferBiochemical engineeringNanotechnologyBusinessMaterials scienceComputer scienceKnowledge managementChemistryEngineering

Abstract

fetched live from OpenAlex

The Open Material Transfer Agreement is a material-transfer agreement that enables broader sharing and use of biological materials by biotechnology practitioners working within the practical realities of technology transfer.

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 score0.279
Threshold uncertainty score0.720

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 integrity0.0010.000
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 head0.005
GPT teacher head0.299
Teacher spread0.294 · 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

Citations67
Published2018
Admission routes1
Has abstractyes

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