{"id":"W2129564368","doi":"10.1145/1645953.1646165","title":"(Not) yet another matcher","year":2009,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Schema matching; Computer science; Schema (genetic algorithms); Matching (statistics); Artificial intelligence; Data mining; Similarity measure; Data integration; 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":[0.01006325,0.001069089,0.001436881,0.002184508,0.001474713,0.004498452,0.002186427,0.003135953,0.02586422],"category_scores_gemma":[0.01994178,0.0009654316,0.00211041,0.002425187,0.001157344,0.01031878,0.00480363,0.002120218,0.01741419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008744235,"about_ca_system_score_gemma":0.001428361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009874688,"about_ca_topic_score_gemma":0.0008733565,"domain_scores_codex":[0.9925129,0.001488482,0.0008565584,0.002832132,0.001883739,0.0004261717],"domain_scores_gemma":[0.9893317,0.001953757,0.0005193816,0.006286688,0.001549549,0.0003588124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00111276,0.0003667597,0.01571637,0.001131255,0.000599965,0.001082772,0.002321038,0.004414262,0.03264305,0.1242163,0.09627963,0.7201157],"study_design_scores_gemma":[0.0001723187,0.0003392401,0.004320595,0.0003251169,0.0004497709,0.003424048,0.001558465,0.05711807,0.07304071,0.08959156,0.7694483,0.0002117443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0284405,0.001134915,0.8974587,0.003673873,0.0009834884,0.0005195313,0.002365967,0.03855269,0.02687034],"genre_scores_gemma":[0.1556368,0.0005321996,0.8048673,0.00299151,0.0001792217,0.0003617315,0.004003296,0.00527654,0.02615146],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02586422,"threshold_uncertainty_score":0.08652443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317223723397408,"score_gpt":0.2521749211032938,"score_spread":0.2290026838693197,"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."}}