{"id":"W3170395146","doi":"10.1002/9781118445112.stat08183","title":"<scp>G</scp>odambe,<scp>V</scp>idyadhar<scp>P</scp>rabhakar","year":2019,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"European Law and Migration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inference; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002771893,0.0006253655,0.001044061,0.003295088,0.002244338,0.005161848,0.001200208,0.001346457,0.1815343],"category_scores_gemma":[0.02059676,0.0003263272,0.0004266514,0.00665627,0.001663198,0.003168559,0.002624464,0.002346363,0.05861782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004406681,"about_ca_system_score_gemma":0.003213969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0158885,"about_ca_topic_score_gemma":0.01753087,"domain_scores_codex":[0.997844,0.0006010742,0.0001137677,0.000562745,0.0007422453,0.0001362381],"domain_scores_gemma":[0.9907883,0.003762261,0.0004492305,0.001071789,0.003510886,0.0004176566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000054066,0.00002949678,0.0006446708,0.0002117077,0.000013748,0.0001179406,0.0001371787,0.0007237498,0.0001615889,0.10647,0.7152972,0.1761387],"study_design_scores_gemma":[0.000021173,0.00001427088,0.001736326,0.0003346877,0.00001616444,0.0001884359,0.0002109211,0.003936905,0.001231196,0.1099668,0.8822943,0.00004889472],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.005788599,0.02049753,0.05969398,0.1483463,0.01485249,0.0001927734,0.01450074,0.002365367,0.7337622],"genre_scores_gemma":[0.135683,0.0247358,0.02953001,0.008773061,0.00549594,0.0003320797,0.01046941,0.002607809,0.7823728],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8184657,"threshold_uncertainty_score":0.6072926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03981364831258069,"score_gpt":0.318126550190956,"score_spread":0.2783129018783753,"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."}}