{"id":"W2159545104","doi":"10.1145/1553374.1553513","title":"BoltzRank","year":2009,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pairwise comparison; Ranking (information retrieval); Computer science; Set (abstract data type); Rank (graph theory); Relevance (law); ENCODE; Function (biology); Information retrieval; Learning to rank; Measure (data warehouse); Data mining; Artificial intelligence; Machine learning; Mathematics","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":[0.002622215,0.002125426,0.002513894,0.006775951,0.002147106,0.005667411,0.003217677,0.002450071,0.07612832],"category_scores_gemma":[0.01379138,0.0008572698,0.001240368,0.007178618,0.001063875,0.006421829,0.002913062,0.002006327,0.07392827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508411,"about_ca_system_score_gemma":0.002988152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004040662,"about_ca_topic_score_gemma":0.008270034,"domain_scores_codex":[0.995156,0.001105966,0.0003909344,0.0006486676,0.00220587,0.0004926382],"domain_scores_gemma":[0.9959091,0.001210808,0.0003183883,0.001430248,0.0009431607,0.0001883059],"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":[0.0002940732,0.0001996815,0.001481042,0.0008173725,0.0001699498,0.0001225397,0.0001243063,0.02469266,0.0016709,0.1304036,0.2643049,0.5757188],"study_design_scores_gemma":[0.0003249419,0.0002291531,0.0006854646,0.0002591496,0.0001177279,0.0005965888,0.0001587265,0.1548984,0.00744129,0.3607417,0.4744104,0.0001364481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008501618,0.006935749,0.775121,0.002248282,0.001603121,0.001357588,0.01403976,0.04767355,0.1425193],"genre_scores_gemma":[0.1428652,0.005425909,0.6347102,0.001451419,0.001265889,0.00164918,0.03866259,0.006787437,0.1671822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07612832,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179954817276005,"score_gpt":0.254509222366211,"score_spread":0.242709674193451,"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."}}