{"id":"W3198935758","doi":"10.1051/0004-6361/202142224","title":"<i>Euclid</i>: Constraining ensemble photometric redshift distributions with stacked spectroscopy","year":2022,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Integrated Electronics Engineering Center, Binghamton University; Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas; National Astronomical Observatory of Japan; Norsk Romsenter; Institut de Física d'Altes Energies; Spanish National Plan for Scientific and Technical Research and Innovation; Staatssekretariat für Bildung, Forschung und Innovation; Fundação para a Ciência e a Tecnologia; Academy of Finland; Agenția Spațială Română; Science and Technology Facilities Council; Agenzia Spaziale Italiana; European Space Agency; National Aeronautics and Space Administration","keywords":"Physics; Photometric redshift; Redshift; Astrophysics; Galaxy; Photometry (optics); Sky; Astronomy; 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":[0.001278969,0.0005538316,0.0003142755,0.0008676097,0.0002497373,0.0008597051,0.0009940042,0.0004221947,0.0006733535],"category_scores_gemma":[0.003386904,0.0003420323,0.0005626981,0.001041871,0.0002990154,0.0007068362,0.001071457,0.0005018418,0.0005447694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004885787,"about_ca_system_score_gemma":0.0004413526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008595037,"about_ca_topic_score_gemma":0.01174624,"domain_scores_codex":[0.9995554,0.0001255333,0.00001804868,0.0001443324,0.0001245329,0.0000322312],"domain_scores_gemma":[0.9985607,0.0001811859,0.0003198985,0.0005741578,0.0002181768,0.0001459374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000645921,0.0001279307,0.1933728,0.0003063091,0.0009088146,0.0003066517,0.0003799945,0.3903216,0.07226776,0.02553527,0.03422656,0.2816004],"study_design_scores_gemma":[0.00003947924,0.0000823981,0.139709,0.00004093053,0.00008035191,0.0003870481,0.00005443835,0.7988768,0.02590635,0.01696311,0.01775118,0.0001088463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3511292,0.001061143,0.6173738,0.0005213164,0.0001246372,0.0000680008,0.01143535,0.01088573,0.007400784],"genre_scores_gemma":[0.6153753,0.0001909483,0.365842,0.0001027385,0.00006114155,0.00004449296,0.0163416,0.0007880582,0.00125364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008595037,"threshold_uncertainty_score":0.01709002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006759439717413952,"score_gpt":0.2027229234503671,"score_spread":0.1959634837329532,"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."}}