{"id":"W3166683655","doi":"10.1038/s41550-021-01372-6","title":"Bayesian analysis of Enceladus’s plume data to assess methanogenesis","year":2021,"lang":"en","type":"article","venue":"Nature Astronomy","topic":"Astro and Planetary Science","field":"Physics and Astronomy","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Université de Recherche Paris Sciences et Lettres; Institut Universitaire de France; Muséum National d'Histoire Naturelle; Ministère de l'Enseignement Supérieur, de la Recherche, de la Science et de la Technologie; Agence Nationale de la Recherche","keywords":"Methanogenesis; Enceladus; Plume; Bayesian probability; Environmental science; Computational biology; Computer science; Biology; Ecology; Astrobiology; Geography; Meteorology; Artificial intelligence; Methane","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002705905,0.0001795516,0.0004100109,0.000208354,0.00007046805,0.00004720804,0.0008723325,0.00008073203,0.002534529],"category_scores_gemma":[0.00001583285,0.0001736763,0.0001526021,0.001645014,0.00003804619,0.0002746654,0.0002940616,0.0003050937,0.00002769031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001194555,"about_ca_system_score_gemma":0.0002431132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001477299,"about_ca_topic_score_gemma":0.00007335268,"domain_scores_codex":[0.9984084,0.00008363363,0.0002792521,0.0006072827,0.0002963892,0.0003249931],"domain_scores_gemma":[0.9982997,0.0001249437,0.0001254279,0.0011351,0.0001263266,0.0001885378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001415567,0.0001161462,0.9267599,0.000005794309,0.001637144,0.000005462748,0.0001236884,0.005697943,0.007003569,0.001335401,0.002175234,0.05512557],"study_design_scores_gemma":[0.0004293694,0.00007107513,0.8076704,0.00003163054,0.002401182,0.000001486089,0.001523336,0.009669954,0.02862963,0.00007890912,0.1488011,0.0006919423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3256836,0.0003544532,0.6571283,0.0004823474,0.0004246626,0.0002316251,0.002737012,0.00003011077,0.01292794],"genre_scores_gemma":[0.9228933,5.277528e-7,0.0742204,0.00008632002,0.0001650804,0.000005334956,0.002144973,0.000007725568,0.0004762765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5972098,"threshold_uncertainty_score":0.9983773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03262319205749668,"score_gpt":0.3010889196450268,"score_spread":0.2684657275875301,"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."}}