{"id":"W4399114754","doi":"10.48550/arxiv.2405.18126","title":"Euclid preparation. Observational expectations for redshift z<7 active galactic nuclei in the Euclid Wide and Deep surveys","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; Perimeter Institute; University of Waterloo","funders":"Fundação para a Ciência e a Tecnologia; Norsk Romsenter; Agenția Spațială Română; Science and Technology Facilities Council; National Astronomical Observatory of Japan; Agenzia Spaziale Italiana; Magyar Tudományos Akadémia; National Aeronautics and Space Administration; European Space Agency; UK Research and Innovation","keywords":"Redshift; Observational study; Astrophysics; Active galactic nucleus; Physics; Astronomy; Psychology; Galaxy; Mathematics; Statistics","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":[0.002865626,0.001441085,0.0007277136,0.002048192,0.0006977957,0.002129951,0.001712321,0.0008103009,0.1374492],"category_scores_gemma":[0.01042902,0.0007060764,0.001092623,0.002515788,0.0003890433,0.001256621,0.001765379,0.001476469,0.119766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363968,"about_ca_system_score_gemma":0.002458324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01239687,"about_ca_topic_score_gemma":0.01546119,"domain_scores_codex":[0.9984659,0.0005066557,0.0001122022,0.0003108991,0.0004710172,0.0001333359],"domain_scores_gemma":[0.9958951,0.0007319085,0.0003735415,0.001250117,0.001228801,0.0005205491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001911262,0.00003356953,0.00414157,0.0003900566,0.00004352547,0.00009594805,0.00009981816,0.005463088,0.0006737417,0.05661281,0.8681588,0.06409603],"study_design_scores_gemma":[0.0000618805,0.00004133885,0.005048377,0.0001535784,0.00001721098,0.0002760049,0.00004886228,0.005883524,0.000919575,0.02676412,0.9607357,0.00004969308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.005712233,0.002415434,0.1473028,0.002339056,0.002526149,0.0008559416,0.6046199,0.01256881,0.2216598],"genre_scores_gemma":[0.04510233,0.002485051,0.2243228,0.001398105,0.0008440415,0.002013156,0.6102195,0.01285845,0.1007566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1374492,"threshold_uncertainty_score":0.4598132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08916789045651406,"score_gpt":0.258979112478429,"score_spread":0.1698112220219149,"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."}}