{"id":"W4295011191","doi":"10.1051/0004-6361/202244795","title":"<i>Euclid</i>: Calibrating photometric redshifts with spectroscopic cross-correlations","year":2023,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Fundação para a Ciência e a Tecnologia; National Astronomical Observatory of Japan; Norsk Romsenter; Academy of Finland; Agenția Spațială Română; Science and Technology Facilities Council; Ministerio de Ciencia e Innovación; Narodowe Centrum Nauki; Agenzia Spaziale Italiana; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Staatssekretariat für Bildung, Forschung und Innovation; Deutsche Forschungsgemeinschaft; National Aeronautics and Space Administration","keywords":"Physics; Astrophysics; Redshift; Photometric redshift; Red shift; Astronomy; Photometry (optics); Apparent magnitude; Galaxy; Stars","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":[0.002188558,0.0004737195,0.0003075647,0.0007489988,0.0004281231,0.001114487,0.001141207,0.0003641706,0.001153358],"category_scores_gemma":[0.009063329,0.0004275903,0.0004728177,0.001053186,0.00065855,0.0008746822,0.001442264,0.0005195247,0.0007388136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009987692,"about_ca_system_score_gemma":0.000675715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01205952,"about_ca_topic_score_gemma":0.01103201,"domain_scores_codex":[0.999008,0.0003768246,0.00004647595,0.0003084994,0.0002072074,0.00005298343],"domain_scores_gemma":[0.9965016,0.0005318859,0.0006224378,0.001649274,0.0004954329,0.0001992601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003731071,0.00008523708,0.3631807,0.0001552659,0.0003837094,0.0001653499,0.0005015532,0.3771341,0.01981116,0.05581805,0.02575278,0.156639],"study_design_scores_gemma":[0.00004080531,0.00007399457,0.09904962,0.00004982944,0.00003566683,0.0002882904,0.00009416119,0.8399395,0.02407388,0.02027775,0.01597465,0.0001018669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.467296,0.0004370747,0.5012246,0.0005547701,0.00009210729,0.00009051705,0.005338143,0.01062246,0.0143444],"genre_scores_gemma":[0.6867377,0.0001050864,0.3066616,0.0001443405,0.00002917444,0.00008169479,0.004310076,0.0009809337,0.0009494048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01205952,"threshold_uncertainty_score":0.02397865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007547618576010061,"score_gpt":0.2227876920964712,"score_spread":0.2152400735204612,"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."}}