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Record W2790639460 · doi:10.6028/nist.sp.1097

Accelerating innovation in 21st century biosciences :

2009· report· en· W2790639460 on OpenAlexfundno aff
Willie E. May, Michael D. Amos, Jennie Hunter‐Cevera

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryNational Institute of Biomedical Imaging and BioengineeringNational Institute of Arthritis and Musculoskeletal and Skin DiseasesPartenariat Canadien Contre Le CancerAgricultural Research ServiceUniversity of California, San FranciscoU.S. Forest ServiceU.S. Geological SurveyNational Centers for Coastal Ocean ScienceNational Oceanic and Atmospheric AdministrationU.S. Food and Drug AdministrationNational Institutes of HealthUnited Soybean BoardJohns Hopkins UniversityBrandeis UniversityArcher Daniels MidlandBiogenSchool of Medicine, Stanford UniversityNational Institute of Standards and TechnologyGenentechU.S. Environmental Protection AgencyIndustrial Technology Research InstituteGeorgia State UniversityU.S. Department of AgricultureUniversity of North Carolina at Chapel HillU.S. Department of EnergyNational Science Foundation
KeywordsData scienceComputer science

Abstract

fetched live from OpenAlex

Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately.Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.

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)0.004
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0080.004
Insufficient payload (model declined to judge)0.0150.006

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 head0.048
GPT teacher head0.325
Teacher spread0.277 · 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

Citations0
Published2009
Admission routes1
Has abstractno

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