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Record W1964705306 · doi:10.1088/1748-0221/6/02/p02005

SHARC: Silicon Highly-segmented Array for Reactions and Coulex used in conjunction with the TIGRESS γ-ray spectrometer

2011· article· en· W1964705306 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Instrumentation · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversity of GuelphUniversité de MontréalSimon Fraser UniversitySaint Mary's UniversityMcMaster UniversityTRIUMF
FundersScience and Technology Facilities CouncilTRIUMF
KeywordsSpectrometerPhysicsDetectorConjunction (astronomy)SiliconAngular resolution (graph drawing)Resolution (logic)Nuclear physicsBeam (structure)Semiconductor detectorOpticsOptoelectronicsComputer scienceAstronomy

Abstract

fetched live from OpenAlex

The combination of gamma-ray spectroscopy and charged-particle spectroscopy is a powerful tool for the study of nuclear reactions with beams of nuclei far from stability. This paper presents a new silicon detector array, SHARC, the Silicon Highly-segmented Array for Reactions and Coulex. The array is used at the radioactive-ion-beam facility at TRIUMF (Canada), in conjunction with the TIGRESS gamma-ray spectrometer, and is built from custom Si-strip detectors utilising a fully digital readout. SHARC has more than 50% efficiency, approximately 1000-strip segmentation, angular resolutions of Delta theta approximate to 1 : 3 deg and Delta phi approximate to 3.5 deg, 25-30 keV energy resolution, and thresholds of 200 keV for up to 25 MeV particles. SHARC is now complete, and the experimental program in nuclear astrophysics and nuclear structure has commenced.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.195

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 head0.030
GPT teacher head0.282
Teacher spread0.252 · 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