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Record W2042555148 · doi:10.1051/epjconf/20125204008

A Quark Matter Contribution to the Cosmic Ray Spectrum

2013· article· en· W2042555148 on OpenAlexaff
Kyle Lawson

Bibliographic record

VenueEPJ Web of Conferences · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsCosmic rayCOSMIC cancer databaseSpectrum (functional analysis)AstrophysicsParticle physicsNuclear physicsAstronomy

Abstract

fetched live from OpenAlex

I will describe a possible dark matter model in which the dark matter is composed of heavy “nuggets” of standard model quarks and antiquarks bound in a high density phase of QCD. If objects of this type are formed early in the universe's history they may provide the observed dark matter content. In this scenario the nuggets are dark not because of their fundamentally weak interactions but because of the incredibly small number density required to explain the observed mass density of the dark matter. The correspondingly small flux of these objects through the earth renders them invisible to conventional high sensitivity dark matter searches intended to detect weakly interacting particles with a flux many orders of magnitude larger. Instead the greatest search potential for dark matter models of this form may come from the largest scale cosmic ray detectors. I will briefly describe the properties of quark nugget dark matter and then use these properties in order to predict the signal they would produce in a variety of cosmic ray detectors.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.007
GPT teacher head0.217
Teacher spread0.209 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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