{"id":"W2237248385","doi":"10.1007/978-3-642-33460-3_48","title":"Learning and Inference in Probabilistic Classifier Chains with Beam Search","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Inference; Classifier (UML); Probabilistic logic; Artificial intelligence; Machine learning; Beam search; Binary classification; Flexibility (engineering); sort; Algorithm; Search algorithm; Mathematics; Support vector machine; Information retrieval","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":[0.01499515,0.001550222,0.004875774,0.003343147,0.002454499,0.004008562,0.006254529,0.004940788,0.01039473],"category_scores_gemma":[0.05001458,0.004856129,0.003809658,0.005949952,0.004654101,0.01072453,0.006081265,0.007489037,0.00356664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002497848,"about_ca_system_score_gemma":0.003160084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0123673,"about_ca_topic_score_gemma":0.01449561,"domain_scores_codex":[0.9914796,0.004816509,0.0005948519,0.001397478,0.00119837,0.0005131058],"domain_scores_gemma":[0.9270695,0.06483648,0.001437644,0.003847884,0.002199425,0.0006089656],"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":[0.0006678761,0.0002949316,0.00279558,0.000347475,0.0003458931,0.0002025665,0.0005573147,0.6065794,0.0009928923,0.1474454,0.009013185,0.2307575],"study_design_scores_gemma":[0.00003661502,0.00002178148,0.00005324666,0.00002401835,0.00002027238,0.00002073807,0.0000149532,0.9011373,0.0002624444,0.0979967,0.0004013357,0.00001064174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004281921,0.0003010268,0.9937896,0.0002778428,0.00002392229,0.00005509502,0.0001086492,0.0005631029,0.0005989092],"genre_scores_gemma":[0.1273734,0.00052597,0.8648536,0.0005216624,0.0002529192,0.0005752939,0.001388452,0.000395199,0.004113509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01499515,"threshold_uncertainty_score":0.07930285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746902263249762,"score_gpt":0.2691181450713802,"score_spread":0.2416491224388826,"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."}}