{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003492585,0.00009585225,0.000110228,0.00001481165,0.00005489508,0.00007534779,0.00004021621,0.00002115187,0.00006424046],"category_scores_gemma":[0.00001085175,0.00007767419,0.00003928912,0.0000789185,0.00004491539,0.0001207804,0.00003090137,0.00009827004,0.00007574953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009178777,"about_ca_system_score_gemma":0.00001409045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004441903,"about_ca_topic_score_gemma":2.661717e-7,"domain_scores_codex":[0.9994673,0.00001435348,0.0001181494,0.0001775746,0.00007909191,0.0001435744],"domain_scores_gemma":[0.9996651,0.0001589696,0.00001606104,0.00008262876,0.00001252539,0.00006471784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009182639,0.00003615365,0.00001638611,0.00003350472,0.00002698859,0.000004096104,0.00008759234,0.000002614966,0.0001741429,0.2528993,0.002263795,0.7444463],"study_design_scores_gemma":[0.0002658849,0.0004333507,0.0003251694,0.00008808597,0.0001060688,0.00001181838,0.0001111757,0.006311201,0.001846617,0.5221605,0.4680121,0.0003280173],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03202635,0.00006250344,0.9658769,0.0001755222,0.0001270979,0.00007814416,0.00002458196,0.0001167274,0.001512118],"genre_scores_gemma":[0.4015575,0.000007258452,0.597293,0.00003597042,0.000416974,0.00001978726,0.00001576383,0.00001970661,0.0006340545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7441183,"threshold_uncertainty_score":0.3167461,"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."}}