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Record W3160234565 · doi:10.15488/10527

Binary gamma-ray pulsars

2021· dissertation· en· W3160234565 on OpenAlexfundno aff
L. Nieder

Bibliographic record

VenueInstitutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersInstitut National de Physique Nucléaire et de Physique des ParticulesScience and Technology Facilities CouncilMinisterium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-WestfalenIstituto Nazionale di AstrofisicaU.S. Department of EnergyVetenskapsrådetMax-Planck-GesellschaftMinistry of Education, Culture, Sports, Science and TechnologyDepartment of Atomic Energy, Government of IndiaNederlandse Organisatie voor Wetenschappelijk OnderzoekObservatoire de Paris, Université de Recherche Paris Sciences et LettresHigh Energy Accelerator Research OrganizationBundesministerium für Bildung und ForschungIstituto Nazionale di Fisica NucleareDeutsche ForschungsgemeinschaftCentre National de la Recherche ScientifiqueJapan Aerospace Exploration AgencyEuropean CommissionCanadian Institute for Advanced ResearchAssociated UniversitiesNational Aeronautics and Space AdministrationNational Science FoundationScience Foundation IrelandAgenzia Spaziale Italiana
KeywordsPulsarGamma rayPhysicsAstrophysicsAstronomy

Abstract

fetched live from OpenAlex

The first pulsar has been detected in 1967 as a seemingly pulsating radio source. Since then, more than 2800 pulsars have been discovered. Most of them also in radio. With the launch of the Fermi Gamma-ray Space Telescope in 2008, more than 250 gamma-ray pulsars have been detected. This thesis concerns the development of methods to directly search the gamma-ray data for pulsars in binary systems, and presents the discovery and analysis of four such pulsars.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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