{"id":"W2913282654","doi":"10.1145/3255771","title":"Session details: Research session 21: entity matching","year":2014,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Session (web analytics); Computer science; Matching (statistics); World Wide Web; Statistics; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006146776,0.002032461,0.003060196,0.001785465,0.002746356,0.006813043,0.001990834,0.004000495,0.6264334],"category_scores_gemma":[0.01110196,0.0005257166,0.002286297,0.0031157,0.0004874683,0.005319078,0.003854878,0.003033679,0.5179057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244643,"about_ca_system_score_gemma":0.003592088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001952657,"about_ca_topic_score_gemma":0.003331828,"domain_scores_codex":[0.9975061,0.0006548691,0.0001318163,0.000782267,0.0006461235,0.0002787992],"domain_scores_gemma":[0.9879642,0.003561497,0.0002925524,0.002406146,0.003201441,0.002574168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003324329,0.0002089755,0.0003503538,0.0003308329,0.00003979265,0.00004961568,0.00005316401,0.0001824741,0.002340261,0.001625409,0.9275162,0.06697042],"study_design_scores_gemma":[0.0001626329,0.0003035069,0.002155333,0.0001597288,0.0001072324,0.0001666561,0.0001556349,0.001580448,0.003178189,0.006240004,0.985748,0.00004258767],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01125559,0.01822047,0.08265259,0.05240711,0.06080417,0.004451753,0.08683651,0.01985579,0.663516],"genre_scores_gemma":[0.04339737,0.00865542,0.02166209,0.005125709,0.01782578,0.00177456,0.07551444,0.003679018,0.8223657],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3735666,"threshold_uncertainty_score":0.5328473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08264994715216234,"score_gpt":0.3641160850528131,"score_spread":0.2814661379006508,"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."}}