{"id":"W4283738049","doi":"10.1051/0004-6361/202244445","title":"<i>Euclid</i>: Forecasts from the void-lensing cross-correlation","year":2022,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Staatssekretariat für Bildung, Forschung und Innovation; 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; Academy of Finland; National Aeronautics and Space Administration; European Space Agency; Deutsche Forschungsgemeinschaft","keywords":"Weak gravitational lensing; Physics; Dark energy; Astrophysics; Galaxy; Cluster analysis; Void (composites); Gravitational lensing formalism; Parameter space; Redshift; Statistical physics; Cosmology; Statistics; Mathematics","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":[0.003413875,0.0009383873,0.0005677809,0.001018069,0.0002569241,0.001300637,0.001122823,0.0008076055,0.00183784],"category_scores_gemma":[0.01241637,0.0003873236,0.0007547267,0.0009837248,0.0003902391,0.001234659,0.0009788969,0.0009188092,0.001232893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009535241,"about_ca_system_score_gemma":0.000992649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02932937,"about_ca_topic_score_gemma":0.02860347,"domain_scores_codex":[0.999405,0.0002240223,0.00003882143,0.0001554257,0.0001136652,0.00006299098],"domain_scores_gemma":[0.9960836,0.001533674,0.0003883102,0.0009790637,0.0006998848,0.0003154303],"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":[0.0004999732,0.00005967383,0.06502641,0.0002135395,0.0002783615,0.0001038579,0.0001612943,0.8206649,0.002262681,0.01158417,0.04929452,0.04985058],"study_design_scores_gemma":[0.00003410798,0.00002848396,0.01157346,0.00003579176,0.0000138828,0.00003774106,0.00003395554,0.974378,0.002173229,0.005399029,0.006252728,0.00003968922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6030279,0.002253598,0.2619448,0.00305113,0.000367606,0.00009179462,0.08510678,0.0289665,0.01518979],"genre_scores_gemma":[0.7714956,0.0004258391,0.1639515,0.0002943244,0.0001244365,0.00008299876,0.0602993,0.001769883,0.001556155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02932937,"threshold_uncertainty_score":0.0583173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007179006217850299,"score_gpt":0.2012834050190872,"score_spread":0.1941043988012369,"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."}}