{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001273688,0.001640489,0.001804383,0.0008137411,0.0009009473,0.0007116841,0.002151414,0.001433105,0.0006681934],"category_scores_gemma":[0.009660071,0.001631962,0.0002952396,0.00118313,0.001550853,0.0004352201,0.0004810189,0.002041034,0.006244341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004863255,"about_ca_system_score_gemma":0.002128318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004941904,"about_ca_topic_score_gemma":0.02925306,"domain_scores_codex":[0.9897685,0.001280301,0.001620196,0.002175454,0.002659817,0.002495727],"domain_scores_gemma":[0.9892865,0.00470562,0.002030591,0.001768575,0.001104903,0.001103789],"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":[0.000004226411,0.0004213593,0.0002296572,0.0002829895,0.0002380338,0.0001149837,0.004681082,0.000009857267,0.000083921,0.1313027,0.8561283,0.006502855],"study_design_scores_gemma":[0.0009728797,0.0005050633,0.0003774673,0.0009243587,0.0003450722,0.000007572822,0.006919809,0.0001489303,0.00003387098,0.003800398,0.9856473,0.0003172951],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001244446,0.004387126,0.01888164,0.0001047417,0.003538811,0.002231946,0.03805888,0.001196107,0.9303563],"genre_scores_gemma":[0.001079438,0.02191445,0.02859032,0.0008838942,0.003715053,0.00007295728,0.0103433,0.001249093,0.9321515],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1295189,"threshold_uncertainty_score":0.9998633,"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."}}