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Record W2889716161

Vývoj neutronového stínění pro podzemní experiment PICASSO pomocí Monte-Carlo výpočtů

2009· article· cs· W2889716161 on OpenAlexaboutno aff
Biskup Bartoloměj

Bibliographic record

VenueCvut DSpace (Czech Technical University) · 2009
Typearticle
Languagecs
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMonte Carlo methodPICASSOMathematicsPhysicsComputer scienceArtStatisticsVisual arts
DOInot available

Abstract

fetched live from OpenAlex

Diplomová práce se věnuje obecně problematice temné hmoty a především experimentálním metodám detekce neutralina jako jednoho z kandidátů temné hmoty. V práci jsou uvedeny nejdůležitější experimenty v dané oblasti a přehled výsledků těchto měření. Je podrobně popsán princip detektoru PICASSO (Project In Canada to Search for Supersymmetric Objects) a základy zpracování experimentálních dat. Těžištěm diplomové práce je studování vlivu různých typů neutronového stínění (voda, polyethylen, voda s příměsí boru...), která jsou dostupná a zároveň se používají v praxi, na úroveň pozadí v detektoru PICASSO a určení nejvhodnějšího typu neutronového stínění z hlediska minimalizace pozadí v detektoru PICASSO.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations0
Published2009
Admission routes1
Has abstractyes

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