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Record W4229661428 · doi:10.1017/s0033822200053583

Radiocarbon Laboratories

2008· article· en· W4229661428 on OpenAlexfundno aff

Bibliographic record

VenueRadiocarbon · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
FundersDivision of Ocean SciencesNational Council for Scientific ResearchNational Institute of Standards and TechnologyLomonosov Moscow State UniversityNational Synchrotron Radiation LaboratoryUniversity of WaterlooKing Saud UniversityInstitute for Health Metrics and EvaluationUniversity of TokyoUniversité de LyonCentre Scientifique de MonacoUniversität WienInstitut Français d’Archéologie OrientaleAustralian Nuclear Science and Technology OrganisationWashington State UniversityNihon UniversityUniversity of PittsburghOhio Wesleyan UniversityUniversity of WashingtonUniversidad de GranadaFlorida State UniversityPeking UniversityUniversity of WaikatoU.S. Department of AgricultureU.S. Geological SurveyKorea Atomic Energy Research InstituteUniversity of MiamiInternational Atomic Energy AgencyDesert Research InstituteUniversity of California, San DiegoYale University
KeywordsRadiocarbon datingAccelerator mass spectrometryListing (finance)ArchaeologyGeographyBusiness

Abstract

fetched live from OpenAlex

This is Radiocarbon's annual list of active radiocarbon laboratories and personnel known to us. Conventional beta-counting facilities are listed in Part I, and accelerator mass spectrometry (AMS) facilities are listed in Part II. Laboratory code designations, used to identify published dates, are given to the left of the listing. (See p 501 for a complete list of past and present lab codes.)

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)0.002
metaresearch head score (Gemma)0.004
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 score0.215
Threshold uncertainty score0.721

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2150.218

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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
Published2008
Admission routes1
Has abstractyes

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