{"id":"W2989107192","doi":"10.1093/mnras/stz3160","title":"Estimating trajectories of meteors: an observational Monte Carlo approach – I. Theory","year":2019,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Astro and Planetary Science","field":"Physics and Astronomy","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Aeronautics and Space Administration","keywords":"Meteor (satellite); Meteoroid; Trajectory; Physics; Monte Carlo method; Algorithm; Computer science; Meteorology; Astronomy; Mathematics; Statistics","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":[0.002452582,0.0005339279,0.000640062,0.001271645,0.000796881,0.001438654,0.00203164,0.001151818,0.002961542],"category_scores_gemma":[0.01059143,0.000738481,0.0009058659,0.0009652973,0.001453245,0.001990601,0.001274856,0.001246462,0.0003074469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002163978,"about_ca_system_score_gemma":0.001456555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02788986,"about_ca_topic_score_gemma":0.01505038,"domain_scores_codex":[0.9994372,0.0001985077,0.00003752975,0.0001159546,0.0001657911,0.00004499912],"domain_scores_gemma":[0.9936546,0.004674819,0.0004803572,0.0004111558,0.0006188973,0.000160212],"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":[0.0000110385,0.000014547,0.001347922,0.00003450485,0.00001387574,0.00001916107,0.00001995876,0.9664446,0.0001730395,0.02706378,0.0003025921,0.004554845],"study_design_scores_gemma":[0.000002504961,0.000003588489,0.0001193001,0.000006962462,0.000001765563,0.000005556753,0.000003114943,0.9945738,0.00007850007,0.004924844,0.0002768668,0.000003195826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01604162,0.0003867385,0.97951,0.0003537993,0.00003653792,0.00004561425,0.0001531376,0.0002278906,0.003244711],"genre_scores_gemma":[0.7023372,0.001605495,0.2899083,0.0002773088,0.0003601386,0.0003352962,0.0007439905,0.0002679381,0.004164364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02788986,"threshold_uncertainty_score":0.05545503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804312430804093,"score_gpt":0.2187889425999349,"score_spread":0.200745818291894,"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."}}