MétaCan
Menu
Back to cohort

Measurement of the Electron Antineutrino Oscillation with 1958 Days of Operation at Daya Bay

2018· article· en· W2891922013 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Review Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsInstitute of Particle Physics
FundersComisión Nacional de Investigación Científica y TecnológicaMinistry of Science and Technology of the People's Republic of ChinaResearch Grants Council, University Grants CommitteeMinisterstvo Školství, Mládeže a TělovýchovyChinese Academy of SciencesAlfred P. Sloan FoundationNational Science CouncilJoint Institute for Nuclear ResearchNational Chiao Tung UniversityNational Natural Science Foundation of ChinaU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsCalibrationNuclear physicsNeutrino oscillationElectron neutrinoElectronOscillation (cell signaling)InverseSpectral lineEnergy (signal processing)NeutrinoAtomic physicsParticle physics

Abstract

fetched live from OpenAlex

We report a measurement of electron antineutrino oscillation from the Daya Bay Reactor Neutrino Experiment with nearly 4 million reactor ${\overline{\ensuremath{\nu}}}_{e}$ inverse $\ensuremath{\beta}$ decay candidates observed over 1958 days of data collection. The installation of a flash analog-to-digital converter readout system and a special calibration campaign using different source enclosures reduce uncertainties in the absolute energy calibration to less than 0.5% for visible energies larger than 2 MeV. The uncertainty in the cosmogenic $^{9}\mathrm{Li}$ and $^{8}\mathrm{He}$ background is reduced from 45% to 30% in the near detectors. A detailed investigation of the spent nuclear fuel history improves its uncertainty from 100% to 30%. Analysis of the relative ${\overline{\ensuremath{\nu}}}_{e}$ rates and energy spectra among detectors yields ${\mathrm{sin}}^{2}2{\ensuremath{\theta}}_{13}=0.0856\ifmmode\pm\else\textpm\fi{}0.0029$ and $\mathrm{\ensuremath{\Delta}}{m}_{32}^{2}=({2.471}_{\ensuremath{-}0.070}^{+0.068})\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}\text{ }\text{ }{\mathrm{eV}}^{2}$ assuming the normal hierarchy, and $\mathrm{\ensuremath{\Delta}}{m}_{32}^{2}=\ensuremath{-}({2.575}_{\ensuremath{-}0.070}^{+0.068})\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}\text{ }\text{ }{\mathrm{eV}}^{2}$ assuming the inverted hierarchy.

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.

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 score0.012
Threshold uncertainty score0.358

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 head0.022
GPT teacher head0.300
Teacher spread0.278 · 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