{"id":"W4386891012","doi":"10.3847/1538-3881/acf5cc","title":"Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets","year":2023,"lang":"en","type":"article","venue":"The Astronomical Journal","topic":"Stellar, planetary, and galactic studies","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Herzberg Institute of Astrophysics; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Agencia Nacional de Investigación y Desarrollo; National Science Foundation; Korea Astronomy and Space Science Institute; Ministério da Ciência, Tecnologia, Inovações e Comunicações; Canadian Space Agency","keywords":"Exoplanet; Orbit (dynamics); Computer science; Orbit determination; Astrobiology; Astronomy; Aerospace engineering; Remote sensing; Physics; Geology; Planet; Engineering; Satellite","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006181081,0.001022523,0.0004241598,0.000607699,0.0004428379,0.0007362138,0.002059783,0.0007829365,0.00610844],"category_scores_gemma":[0.002785096,0.0005451186,0.0008815696,0.000380523,0.0003741425,0.0009998459,0.0009779745,0.0009960965,0.001270486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008491761,"about_ca_system_score_gemma":0.001193427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02321715,"about_ca_topic_score_gemma":0.02357742,"domain_scores_codex":[0.999808,0.00003829219,0.00001459742,0.00004251907,0.00006736228,0.00002935177],"domain_scores_gemma":[0.9993381,0.0002777355,0.00008878908,0.0001131945,0.0001242624,0.00005795252],"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":[0.0002365779,0.0001039251,0.01126031,0.0001026149,0.0001235401,0.0001589033,0.0001274455,0.9276548,0.003738935,0.00456603,0.01582018,0.0361068],"study_design_scores_gemma":[0.0000175222,0.000007849501,0.0003135705,0.000004095288,0.000003321723,0.00001324338,0.000005419744,0.9963994,0.0008428754,0.0007846138,0.00159994,0.000008152761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2385058,0.000294277,0.6208615,0.0006385694,0.0002430404,0.0002865916,0.01151897,0.1181721,0.009478986],"genre_scores_gemma":[0.6611894,0.0002128491,0.308808,0.0002815977,0.00005759927,0.0004464003,0.01352962,0.01019362,0.00528084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02321715,"threshold_uncertainty_score":0.04616398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639836549953508,"score_gpt":0.2564643264843177,"score_spread":0.2300659609847826,"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."}}