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Z-chamber of the CMD-3 detector in the reconstruction of the track longitudinal coordinate

2017· article· en· W2741896611 on OpenAlexaff
R.R. Akhmetshin, Artem Amirkhanov, A. V. Anisenkov, V.M. Aulchenko, V.Sh. Banzarov, N.S. Bashtovoy, D. E. Berkaev, A. Bondar, A.V. Bragin, S.I. Eidelman, D. Epifanov, L.B. Epshteyn, A.L. Erofeev, G.V. Fedotovich, S.E. Gayazov, A.A. Grebenuk, S.S. Gribanov, D.N. Grigoriev, F.V. Ignatov, V. L. Ivanov, S.V. Karpov, V. F. Kazanin, A. S. Kasaev, I. A. Koop, A. Korobov, A. Kozyrev, E.A. Kozyrev, P. Krokovny, A.E. Kuzmenko, A. Kuzmin, I.B. Logashenko, А. П. Лысенко, P.A. Lukin, K. Mikhailov, V.S. Okhapkin, Yu.N. Pestov, E. A. Perevedentsev, A. S. Popov, G.P. Razuvaev, Yu. A. Rogovsky, A.A. Ruban, N.M. Ryskulov, A.E. Ryzhenenkov, Yu. M. Shatunov, V. Shebalin, D.N. Shemyakin, B. Shwartz, D. B. Shwartz, A.L. Sibidanov, E. P. Solodov, В.М. Титов, A.A. Talyshev, A.I. Vorobiov, Yu. V. Yudin

Bibliographic record

VenueJournal of Instrumentation · 2017
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Victoria
FundersNuclear PhysicsRussian Foundation for Basic Research
KeywordsDetectorPhysicsTrack (disk drive)CalibrationAnnihilationColliderNuclear physicsCathodeHadronIonization chamberOpticsComputational physicsIonizationComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Since 2010 the CMD-3 detector has been collecting data at the e + e − collider VEPP-2000 in the Budker Institute of Nuclear Physics. One of the main goals of experiments with CMD-3 detector is the precise measurement of the cross sections of the e + e − annihilation into hadrons. For a large number of processes the main source of systematic uncertainty in cross sections determination due to accuracy of polar angles determination of the tracks. Z-chamber is used for the reconstruction of the track longitudinal coordinate which is with low systematic uncertainty. The measurement of longitudinal coordinates is performed by the collecting of the charge which is induced on the strip cathodes of the Z-chamber. The algorithms of the reconstruction of cathodes clusters and calibration procedure are presented.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.004

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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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 designBench or experimental
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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