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Record W2083671069 · doi:10.1017/s1743921312024593

A high-energy catalogue of Galactic supernova remnants and pulsar wind nebulae

2012· article· en· W2083671069 on OpenAlexafffund
Samar Safí-Harb, Gilles Ferrand, Heather Matheson

Bibliographic record

VenueProceedings of the International Astronomical Union · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Manitoba
FundersCanadian Space AgencyNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Theoretical Astrophysics
KeywordsPulsarPhysicsSupernovaNeutron starAstrophysicsAstronomyStars

Abstract

fetched live from OpenAlex

Abstract Motivated by the wealth of past, existing, and upcoming X-ray and gamma-ray missions, we have developed the first public database of high-energy observations of all known Galactic Supernova Remnants (SNRs): http://www.physics.umanitoba.ca/snr/SNRcat . The catalogue links to, and complements, other existing related catalogues, including Dave Green's radio SNRs catalogue. We here highlight the features of the high-energy catalogue, including allowing users to filter or sort data for various purposes. The catalogue is currently targeted to Galactic SNR observations with X-ray and gamma-ray missions, and is timely with the upcoming launch of X-ray missions (including Astro-H in 2014). We are currently developing the existing database to include an up-to-date Pulsar Wind Nebulae (PWNe)-dedicated webpage, with the goal to provide a global view of PWNe and their associated neutron stars/pulsars. This extensive database will be useful to both theorists to apply their models or design numerical simulations, and to observers to plan future observations or design new instruments. We welcome input and feedback from the SNR/PWN/neutron stars community.

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 score0.354
Threshold uncertainty score0.370

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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