{"id":"W4312730985","doi":"10.1109/icpr56361.2022.9956503","title":"Embedded Spherical Topic Models for Supervised Learning","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Discriminative model; Inference; Topic model; Artificial intelligence; Metadata; Probabilistic logic; Machine learning; Graph; Graphical model; Supervised learning; Information retrieval; Theoretical computer science; Artificial neural network","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003705357,0.0001993633,0.0001997955,0.0001670066,0.000332249,0.000228451,0.001104173,0.00005481694,0.004471363],"category_scores_gemma":[0.00005902073,0.0002251845,0.0001448995,0.0001587609,0.00002067685,0.0004341223,0.0004562545,0.0004779173,0.00009009153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190297,"about_ca_system_score_gemma":0.0001137846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004174665,"about_ca_topic_score_gemma":0.000008552537,"domain_scores_codex":[0.997691,0.0001776241,0.0004020029,0.0006771034,0.0007390633,0.0003131966],"domain_scores_gemma":[0.9990028,0.0001287713,0.0001686796,0.0003124033,0.0002890613,0.00009829891],"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":[0.0001114872,0.0003729153,0.0007540155,0.00003588988,0.0001176561,0.0000416129,0.001980733,0.02280166,0.001714153,0.05351843,0.001445385,0.9171061],"study_design_scores_gemma":[0.0008575377,0.0002471005,0.00008751561,0.00002416184,0.000006884711,0.00001753165,0.0004034408,0.9568717,0.0004340842,0.03831054,0.002454094,0.0002854071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04057856,0.00001345858,0.9438595,0.003345244,0.001614109,0.0003641683,0.00009400168,0.0002084232,0.009922533],"genre_scores_gemma":[0.9833555,0.00001754351,0.01122346,0.002422061,0.000228902,0.0005701575,0.0003099646,0.00002247328,0.001849918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.942777,"threshold_uncertainty_score":0.9964387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209966848680453,"score_gpt":0.3002750955334217,"score_spread":0.1792784106653764,"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."}}