{"id":"W1782140593","doi":"10.48550/arxiv.1202.3706","title":"A Framework for Optimizing Paper Matching","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Matching (statistics); Pairwise comparison; Computer science; Set (abstract data type); Domain (mathematical analysis); Frame (networking); Measure (data warehouse); Integer (computer science); Blossom algorithm; Machine learning; Artificial intelligence; Data mining; Information retrieval; Mathematics; Statistics","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":[0.0115429,0.001943349,0.002470038,0.003522957,0.001463126,0.003305476,0.004332575,0.003053698,0.008368289],"category_scores_gemma":[0.02730287,0.00113538,0.00177825,0.00562432,0.001980735,0.005138568,0.002959076,0.002578183,0.002194967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003475914,"about_ca_system_score_gemma":0.004153951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003869865,"about_ca_topic_score_gemma":0.00339927,"domain_scores_codex":[0.9911679,0.004187489,0.0004557376,0.001766163,0.001944021,0.0004787204],"domain_scores_gemma":[0.9912434,0.004545794,0.0009869771,0.001124635,0.001580008,0.0005191537],"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":[0.0002178903,0.0002924167,0.001790068,0.0003949728,0.0001459776,0.0001273856,0.0003575843,0.4998837,0.002064256,0.1471647,0.01650342,0.3310575],"study_design_scores_gemma":[0.00006627751,0.000148948,0.0004037554,0.00004279577,0.00003942441,0.00009607962,0.00005490864,0.8269725,0.001306311,0.1616061,0.009230902,0.00003206796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003237351,0.0004429781,0.9927703,0.0005359754,0.00007479528,0.0001457882,0.0001671448,0.0004179925,0.002207712],"genre_scores_gemma":[0.1482671,0.000762248,0.8397241,0.0003228198,0.0003478565,0.0007398989,0.0008152902,0.0003962677,0.008624219],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0115429,"threshold_uncertainty_score":0.06104541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08513667651695354,"score_gpt":0.2094413763710582,"score_spread":0.1243046998541047,"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."}}