{"id":"W1629309337","doi":"10.1051/0004-6361/201525820","title":"Planck 2015 results. VIII. High Frequency Instrument data processing: Calibration and maps","year":2016,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Cosmology and Gravitation Theories","field":"Physics and Astronomy","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University; University of Toronto","funders":"Science and Technology Facilities Council; Centre National de la Recherche Scientifique; China Scholarship Council; Tekes; Centre National d’Etudes Spatiales; Max-Planck-Gesellschaft; UK Space Agency; Institut National de Physique Nucléaire et de Physique des Particules; National Aeronautics and Space Administration","keywords":"Physics; Planck; Cosmic microwave background; CMB cold spot; Astrophysics; Sky; Calibration; Amplitude; Remote sensing; Computational physics; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003228882,0.00210883,0.0009598433,0.002947027,0.001107903,0.003131696,0.002505774,0.001659047,0.08323827],"category_scores_gemma":[0.008387375,0.0009938282,0.00248793,0.003462431,0.0005308449,0.002604144,0.002726481,0.001467556,0.1057937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317021,"about_ca_system_score_gemma":0.002352499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01739971,"about_ca_topic_score_gemma":0.01271953,"domain_scores_codex":[0.9975508,0.0003161257,0.0001985013,0.0003711463,0.001224813,0.0003385757],"domain_scores_gemma":[0.9960722,0.0004121121,0.0002652147,0.001427468,0.001574849,0.0002481833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002219522,0.00005362205,0.002944017,0.0003891394,0.0002391408,0.00009810016,0.00008326319,0.00274224,0.001358215,0.003714185,0.9600258,0.02813021],"study_design_scores_gemma":[0.0001452913,0.00004386531,0.02137154,0.0002046517,0.0001739093,0.0001442098,0.0000896789,0.004416609,0.01045992,0.01030905,0.9525019,0.0001394053],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.005816536,0.0007979315,0.02810717,0.002151645,0.001910338,0.0002746243,0.8725583,0.03503377,0.05334961],"genre_scores_gemma":[0.03973851,0.0007191796,0.05717402,0.0008211377,0.0007290167,0.0008376975,0.8431055,0.01931305,0.03756178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08323827,"threshold_uncertainty_score":0.2784598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08007575159708892,"score_gpt":0.3001787733112283,"score_spread":0.2201030217141394,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}