MétaCan
Menu
Back to cohort
Record W6964527083 · doi:10.25919/w37t-nw98

Spectra and Polarisation In Cutouts of Extragalactic sources from RACS First Data Release (SPICE-RACS-DR1)

2023· dataset· en· W6964527083 on OpenAlexaff

Bibliographic record

VenueCSIRO · 2023
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpectral lineFunction (biology)Data collectionStokes parametersPolynomialNoticeFlux (metallurgy)Faraday effect

Abstract

fetched live from OpenAlex

*NOTICE* July 2024: We have discovered an error in the flux density of Stokes Q and U in this data release. Full details are provided in a corrigendum in PASA (see link below). If you are reading this notice the catalogue, spectra and images in this collection have been corrected. The data in the linked 'full' DAP collection are also corrected. Users of the old data can apply their own correction by multiplying Stokes Q and U by 2 for correct results. However, we recommend using the updated products provided here. Users can confirm they are using updated files by inspecting the HISTORY card in the FITS headers. Spectra and Polarisation In Cutouts of Extragalactic sources from RACS (SPICE-RACS) is a project to derive an all-Southern-sky, linear polarisation map from the Rapid ASKAP Continuum Survey (RACS). Through this project, we will derive the following products: - Cutout image cubes in Stokes I, Q, and U around every source detected in total intensity as a function of frequency - Polarisation spectra as a function of frequency and Faraday depth for every component detected in total intensity - A catalogue summarising the polarisation properties, including rotation measure (RM), for every component. This collection represents a subset of the data presented in SPICE-RACS-DR1 (Thomson et al. 2023). This subset refers to the 24680 components ('cut') (out of the 'full' 105912) for which a reliable polynomial fit could be derived for the Stokes I spectra that were extracted. This collection contains the 'cut' subset, please see the linked collections for the 'full' data. Our final data products are as follows: spice-racs.dr1.corrected.{cut,full}.xml: - Our component catalogue in VOTable format for the first (cut) and second (full) sets of components described above. The former is included with this deposit. {source_id}.cutout.{image,weights}.*.fits: - Image and weights cube cutout in Stokes I, Q, and U cutout around a RACS-low source identified by ‘source_id’. The data dimensions are J2000 right ascension and declination, Stokes parameter, and frequency. {cat_id}_polspec.fits: - Polarisation spectra extracted on the position of the RACS-low component identified by ‘cat_id’ in POLSPECTRA format. Each table contains a single row, corresponding to the component. spice_racs_dr1_polspec_{cut,full}.tar: - A tarball containing all of the POLSPECTRA for the 'cut' and second 'full' sets of components described above. These are available in the linked collections. Recommended catalogue subsets: Our 'full' catalogue contains extracted polarisation data for all 105912 Stokes I components detected in our DR1 region. It is important to note that the majority of these do not have reliably detected polarised emission. When using this catalogue please use the provided flags, and uncertainty/quality metrics to derive a subset suitable for your science goals. If you're unsure where to start, these are our recommended basic subsets: - 'goodI' : Where our Stokes I model is reliable -- channel_flag and stokesI_fit_flag are False (this produces the 'cut' catalogue from the 'full'). - 'goodL': Where we have a reliable detection of polarised signal, but not necessarily a reliable RM -- goodI plus leakage_flag is False and snr_polint >= 5. - 'goodRM': Where the RM is reliable -- goodL plus snr_flag is False.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.664
Threshold uncertainty score0.990

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 science0.0010.001
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 head0.045
GPT teacher head0.234
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCSIROSame topicForest Ecology and Biodiversity StudiesFrench-language works237,207