{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004259909,0.001616589,0.001943031,0.002068403,0.0006359151,0.00150033,0.002971706,0.001712574,0.002445438],"category_scores_gemma":[0.01347733,0.0009643009,0.002177947,0.002441883,0.001413765,0.003327877,0.002094871,0.003285808,0.00148535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665157,"about_ca_system_score_gemma":0.001346103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006446209,"about_ca_topic_score_gemma":0.008592916,"domain_scores_codex":[0.9973822,0.001321946,0.0001393391,0.0006578219,0.000361227,0.0001374347],"domain_scores_gemma":[0.9920607,0.005604933,0.0006316986,0.0008394161,0.0007108914,0.0001524294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001658852,0.0001203368,0.001942908,0.0002425804,0.0002445651,0.0001095096,0.0003735094,0.7601836,0.001495729,0.1098163,0.005005688,0.1202994],"study_design_scores_gemma":[0.000005666742,0.00001098244,0.00009677665,0.0000079868,0.000007344788,0.00001008811,0.00001010063,0.9624689,0.0001534662,0.0366476,0.0005739424,0.000007195627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004170207,0.0004092172,0.9943387,0.0001577829,0.00002348984,0.00003479363,0.0001695068,0.0003604544,0.0003357999],"genre_scores_gemma":[0.4808171,0.002355267,0.5016296,0.000574509,0.0006943138,0.001193301,0.004657315,0.0006910917,0.007387529],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006446209,"threshold_uncertainty_score":0.02252883,"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."}}