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Record W2039421290 · doi:10.1103/physrevlett.86.3763

Measurement of the Resonant<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi mathvariant="italic">d</mml:mi><mml:mi mathvariant="italic">μ</mml:mi><mml:mi mathvariant="italic">t</mml:mi></mml:math>Molecular Formation Rate in Solid HD

2001· article· lv· W2039421290 on OpenAlexaff
T. A. Porcelli, A. Adamczak, J. M. Bailey, G. Beer, J. L. Douglas, М. П. Файфман, Masahiro Fujiwara, T. M. Huber, P. Kammel, S. K. Kim, P. Knowles, A. R. Kunselman, M. Maier, V. E. Markushin, G. M. Marshall, G. R. Mason, F. Mulhauser, Å. Olin, C. Petitjean, J. Zmeskal

Bibliographic record

VenuePhysical Review Letters · 2001
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsTRIUMFUniversity of British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsPhysicsFusionMuonAlgorithmNuclear physicsPhilosophyComputer science

Abstract

fetched live from OpenAlex

Measurements of muon-catalyzed $\mathrm{dt}$ fusion ( $d\ensuremath{\mu}t{\ensuremath{\rightarrow}}^{4}\mathrm{He}+n+{\ensuremath{\mu}}^{\ensuremath{-}}$) in solid HD have been performed. The theory describing the energy dependent resonant molecular formation rate for the reaction $\ensuremath{\mu}t+\mathrm{HD}\ensuremath{\rightarrow}[(d\ensuremath{\mu}t)\mathrm{pee}{]}^{*}$ is compared to experimental results in a pure solid HD target. Constraints on the rates are inferred through the use of a Monte Carlo model developed specifically for the experiment. From the time-of-flight analysis of fusion events in 16 and $37\ensuremath{\mu}\mathrm{g}\ifmmode \dot{}\else \.{}\fi{}{\mathrm{cm}}^{\ensuremath{-}2}$ targets, an average formation rate consistent with $0.897\ifmmode\pm\else\textpm\fi{}(0.046{)}_{\mathrm{stat}}\ifmmode\pm\else\textpm\fi{}(0.166{)}_{\mathrm{syst}}$ times the theoretical prediction was obtained.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.017
GPT teacher head0.249
Teacher spread0.233 · 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.

Study designTheoretical or conceptual
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

Citations15
Published2001
Admission routes1
Has abstractyes

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