Planck 2015 results XXVII. The second Planck catalogue of Sunyaev-Zeldovich sources
Bibliographic record
Abstract
Acknowledgements. The Planck Collaboration acknowledges the support of: \nESA; CNES and CNRS/INSU-IN2P3-INP (France); ASI, CNR, and INAF \n(Italy); NASA and DoE (USA); STFC and UKSA (UK); CSIC, MINECO, \nJA, and RES (Spain); Tekes, AoF, and CSC (Finland); DLR and MPG \n(Germany); CSA (Canada); DTU Space (Denmark); SER/SSO (Switzerland); \nRCN (Norway); SFI (Ireland); FCT/MCTES (Portugal); ERC and PRACE (EU). \nA description of the Planck Collaboration and a list of its members, indicating \nwhich technical or scientific activities they have been involved in, can be found at \nhttp://www.cosmos.esa.int/web/planck/planck-collaboration. We \nthank Ian McCarthy for providing images and profiles of simulated clusters from \ncosmo-OWLS. This research has made use of the NASA/IPAC Extragalactic \nDatabase (NED), which is operated by the Jet Propulsion Laboratory, California \nInstitute of Technology, under contract with the National Aeronautics and Space \nAdministration, and the SIMBAD database, operated at CDS, Strasbourg, France \nThis research made use of data retrieved from SDSS-III. Funding for SDSSIII has been provided by the Alfred P. Sloan Foundation, the Participating \nInstitutions, the National Science Foundation, and the US Department of \nEnergy Office of Science; the SDSS-III web site is http://www.sdss3.org/. \nThis research has made use of data processed by the Centre d’Analyse de \nDonnées Étendues (http://cade.irap.omp.eu/) and has made use of the \nHEALPix pixelization software (http://healpix.sourceforge.net; Górski \net al. 2005). Some of this work was performed using the Darwin Supercomputer \nof the University of Cambridge High Performance Computing Service (http: \n//www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research \nInfrastructure Funding from the Higher Education Funding Council for England \nand funding from the Science and Technology Facilities Council.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.159 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".