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Record W2604357159 · doi:10.1017/pasa.2017.13

Low-Frequency Spectral Energy Distributions of Radio Pulsars Detected with the Murchison Widefield Array

2017· article· en· W2604357159 on OpenAlexaff
Tara Murphy, D. L. Kaplan, M. E. Bell, J. R. Callingham, S. Croft, S. Johnston, Dougal Dobie, Andrew Zic, J. D. Hughes, C. Lynch, P. J. Hancock, N. Hurley‐Walker, E. Lenc, K. S. Dwarakanath, Bi‐Qing For, B. M. Gaensler, L. Hindson, M. Johnston‐Hollitt, A. D. Kapińska, B. McKinley, John Morgan, A. R. Offringa, P. Procopio, L. Staveley‐Smith, R. B. Wayth, Chonghao Wu, Q. Zheng

Bibliographic record

VenuePublications of the Astronomical Society of Australia · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersScience and Technology Facilities CouncilJet Propulsion LaboratoryDepartment of Industry and Science, Australian GovernmentCommonwealth Scientific and Industrial Research OrganisationAustralian GovernmentCurtin University of TechnologyCalifornia Institute of TechnologyNvidiaNational Aeronautics and Space AdministrationHarvard UniversityAstronomy Australia LimitedNational Science Foundation
KeywordsPulsarPhysicsRadio telescopeRadio spectrumTelescopeSkyAstrophysicsFlux (metallurgy)Low frequencyRadio frequencyAstronomyTelecommunicationsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Abstract We present low-frequency spectral energy distributions of 60 known radio pulsars observed with the Murchison Widefield Array telescope. We searched the GaLactic and Extragalactic All-sky Murchison Widefield Array survey images for 200-MHz continuum radio emission at the position of all pulsars in the Australia Telescope National Facility (ATNF) pulsar catalogue. For the 60 confirmed detections, we have measured flux densities in 20 × 8 MHz bands between 72 and 231 MHz. We compare our results to existing measurements and show that the Murchison Widefield Array flux densities are in good agreement.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.451

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations36
Published2017
Admission routes1
Has abstractyes

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