{"id":"W3201096617","doi":"10.1142/s0218001421510101","title":"An Extension of the Gamma Test Statistics to Binary Variables and Some Applications","year":2021,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Control Systems and Identification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Wilfrid Laurier University; University of Alberta; University of Ottawa","funders":"","keywords":"Heuristics; Extension (predicate logic); Binary classification; Binary number; Computer science; Artificial intelligence; Variance (accounting); Algorithm; Machine learning; Mathematics; Mathematical optimization; Arithmetic; Support vector machine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001623461,0.00005891122,0.00009334666,0.00008056535,0.00003649559,0.00007927892,0.0001050911,0.00002744462,0.00004770134],"category_scores_gemma":[0.00009182342,0.00004984505,0.00002518644,0.00007242001,0.00002837889,0.0001631553,0.00002263923,0.00007552859,0.000007970812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001738845,"about_ca_system_score_gemma":0.00001899628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001653173,"about_ca_topic_score_gemma":0.00004059351,"domain_scores_codex":[0.9992555,0.00002869555,0.0004016064,0.00008556199,0.0001738798,0.00005472887],"domain_scores_gemma":[0.9990577,0.0001021512,0.000121596,0.00007894741,0.0005844795,0.00005511584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001123966,0.0001002014,0.0008338616,0.00002704449,0.00003394185,0.000008502378,0.0002605029,0.0009139722,0.3046316,0.0006719411,0.00008631534,0.6924209],"study_design_scores_gemma":[0.0004119806,0.0004269708,0.07857127,0.001421163,0.000195078,0.0007936473,0.00335059,0.1678185,0.6126414,0.1294442,0.004234741,0.0006904299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5698909,0.000263202,0.4283255,0.0004850849,0.0006906903,0.0001065403,0.0002074547,0.000009149639,0.00002150539],"genre_scores_gemma":[0.998412,0.0002560362,0.0009412756,0.000100625,0.0002516744,0.00000563366,0.00001496787,0.000006896248,0.00001095505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6917305,"threshold_uncertainty_score":0.2032622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03791919094875298,"score_gpt":0.2797782703107369,"score_spread":0.2418590793619839,"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."}}