{"id":"W2935126934","doi":"10.1145/3181672","title":"Bi-Level Thresholding","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thresholding; False positive paradox; Artificial intelligence; Computer science; Pattern recognition (psychology); Balanced histogram thresholding; Accelerometer; Speech recognition; Histogram","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":[0.004827909,0.001432206,0.001678799,0.002241965,0.0009763355,0.004641833,0.002520694,0.001798235,0.007384844],"category_scores_gemma":[0.02908354,0.0006879905,0.001116891,0.003268249,0.001233554,0.003586919,0.002704724,0.002255493,0.007445926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009901522,"about_ca_system_score_gemma":0.001526395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003178887,"about_ca_topic_score_gemma":0.003446291,"domain_scores_codex":[0.9930961,0.0009203799,0.0009164262,0.00176817,0.002824754,0.0004742017],"domain_scores_gemma":[0.9844446,0.005434634,0.001175752,0.00399394,0.004706992,0.0002440172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008186029,0.0001641353,0.006865877,0.0009373182,0.0001903087,0.0002235539,0.0006973303,0.01294637,0.06979788,0.01154494,0.01673864,0.8790751],"study_design_scores_gemma":[0.0001676051,0.001004006,0.02051107,0.0006717606,0.0004716297,0.002984508,0.001051673,0.5511035,0.2404603,0.07465129,0.1064415,0.0004810641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01565985,0.001179051,0.9692304,0.0003611374,0.0003888184,0.0002363736,0.0004746091,0.007514368,0.004955363],"genre_scores_gemma":[0.2089321,0.0006763458,0.7796749,0.0008231584,0.0001377473,0.0004773788,0.001505579,0.001626341,0.006146398],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007384844,"threshold_uncertainty_score":0.02553266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05449439370813911,"score_gpt":0.2935625069466191,"score_spread":0.23906811323848,"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."}}