{"id":"W2981183195","doi":"10.48550/arxiv.1910.08857","title":"LRP2020: Astrostatistics in Canada","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Training (meteorology); White paper; Norm (philosophy); Statistical analysis; Professional development; Political science; Library science; Psychology; Medical education; Public relations; Geography; Computer science; Pedagogy; Statistics; Engineering; Mathematics; Medicine; Meteorology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001571491,0.0002574811,0.000385041,0.0002526377,0.0000392636,0.00009721,0.002766332,0.0001128811,0.00005236775],"category_scores_gemma":[0.00004273895,0.0003164537,0.00008026068,0.0007364944,0.00003277093,0.0002619861,0.002688691,0.0005786751,0.0001461267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001342142,"about_ca_system_score_gemma":0.003097603,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7771429,"about_ca_topic_score_gemma":0.8932121,"domain_scores_codex":[0.9980762,0.0001168507,0.0002229963,0.001048054,0.0001472625,0.0003886457],"domain_scores_gemma":[0.997619,0.0001278959,0.0002271277,0.001782321,0.00009562384,0.0001480822],"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":[0.000008411731,0.00003463132,0.09259239,0.00006385978,0.00008266706,0.001518529,0.00006014008,0.8150588,0.000002926529,0.08426353,0.005617417,0.0006966654],"study_design_scores_gemma":[0.0003022262,0.00001274906,0.01988777,0.00005745432,0.00003863271,0.000002060114,0.00005769209,0.9718093,0.00001209662,0.005983682,0.001423012,0.000413289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1106041,0.00002814187,0.8857682,0.0001098465,0.0008288539,0.000198835,0.0001832579,0.00005663366,0.002222144],"genre_scores_gemma":[0.994857,0.00004378796,0.003642587,0.0001677892,0.00002174467,3.14736e-7,0.00007793871,0.00001156207,0.001177258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8842529,"threshold_uncertainty_score":0.9999288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04302887935181211,"score_gpt":0.1593758620482718,"score_spread":0.1163469826964597,"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."}}