{"id":"W2010748082","doi":"10.1007/s10044-013-0323-0","title":"Beyond hybrid generative discriminative learning: spherical data classification","year":2013,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Categorization; Generative grammar; Computer science; Probabilistic logic; Artificial intelligence; Generative model; Machine learning; Pattern recognition (psychology); Contrast (vision)","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":[],"consensus_categories":[],"category_scores_codex":[0.004131162,0.001274493,0.002672211,0.001968614,0.0007709001,0.003087469,0.003700227,0.002249992,0.003995992],"category_scores_gemma":[0.01792662,0.001098888,0.00183145,0.003488425,0.002265318,0.00489655,0.005349803,0.003317827,0.002283456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009921021,"about_ca_system_score_gemma":0.001202131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003221859,"about_ca_topic_score_gemma":0.004324735,"domain_scores_codex":[0.9973782,0.001230825,0.0001337284,0.0005340115,0.0005723059,0.0001509343],"domain_scores_gemma":[0.9910779,0.004445433,0.000551342,0.002487015,0.001134559,0.0003037253],"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":[0.000253162,0.0001419974,0.003111938,0.0004132294,0.0002346151,0.0001158207,0.0003076881,0.2093005,0.004589912,0.2760083,0.01019463,0.4953282],"study_design_scores_gemma":[0.000008349382,0.00002043827,0.0003020073,0.00002473531,0.00001425858,0.00006885214,0.00002868635,0.8596389,0.0008610425,0.1367732,0.002240267,0.0000193352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002276944,0.0003272049,0.9964315,0.0002092215,0.00001701084,0.00001347248,0.00005699791,0.0002218811,0.000445842],"genre_scores_gemma":[0.2500074,0.002071907,0.7385632,0.0007471319,0.0004216905,0.0002015521,0.001423394,0.000686308,0.005877401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004131162,"threshold_uncertainty_score":0.02184796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04171890366918496,"score_gpt":0.3063761530275938,"score_spread":0.2646572493584088,"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."}}