{"id":"W4402976360","doi":"10.1051/0004-6361/202348389","title":"<i>Euclid</i> preparation","year":2024,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; McGill University","funders":"European Space Agency; Agenzia Spaziale Italiana; Fundação para a Ciência e a Tecnologia; Dipartimenti di Eccellenza; Magyar Tudományos Akadémia; Horizon 2020 Framework Programme; Aix-Marseille Université; Agenția Spațială Română; Centre National d’Etudes Spatiales; Norsk Romsenter; National Astronomical Observatory of Japan; European Commission; National Aeronautics and Space Administration; Ministerio de Ciencia, Innovación y Universidades","keywords":"Physics; Covariance; Astrophysics; Sample (material); Analysis of covariance; Statistical physics; Statistics; Astronomy; Mathematics; Thermodynamics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003875139,0.002182398,0.00164238,0.002973589,0.001478834,0.005509428,0.004334575,0.001664184,0.491834],"category_scores_gemma":[0.02480344,0.001094657,0.00174099,0.005042935,0.001175601,0.003095958,0.004763423,0.002653727,0.3811931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002472456,"about_ca_system_score_gemma":0.003367411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009238117,"about_ca_topic_score_gemma":0.00965359,"domain_scores_codex":[0.996426,0.0009246901,0.0003299091,0.0007214833,0.001308879,0.0002890613],"domain_scores_gemma":[0.9864096,0.001847883,0.000713004,0.005686513,0.004305828,0.001037025],"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":[0.0001092226,0.000007695535,0.0002049623,0.0001739903,0.00001842928,0.00008047139,0.00006375829,0.0003025286,0.0005297653,0.01607824,0.9675614,0.01486953],"study_design_scores_gemma":[0.000036915,0.00001824757,0.0006157037,0.0001027285,0.000008577139,0.0001349472,0.00002818422,0.0005165041,0.001148839,0.006553772,0.9908065,0.00002892397],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0023433,0.001797419,0.08284131,0.008677531,0.01720423,0.0007058774,0.5516704,0.04223913,0.2925209],"genre_scores_gemma":[0.02902456,0.002379406,0.09933856,0.003804559,0.004863442,0.001651958,0.6626284,0.04848681,0.1478224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.491834,"threshold_uncertainty_score":0.7248372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679295568011265,"score_gpt":0.2847187122871597,"score_spread":0.267925756607047,"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."}}