{"id":"W2406112091","doi":"","title":"Agent-based Stochastic Simulation of Schema Matching.","year":2014,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Schema matching; Schema (genetic algorithms); Computer science; Matching (statistics); Schema evolution; Optimal matching; Data mining; Theoretical computer science; Database schema; Information retrieval; Data integration; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002422916,0.0001925829,0.0002430022,0.00020849,0.00003977883,0.00004405145,0.0002691424,0.00007808443,0.000001519538],"category_scores_gemma":[0.0006863875,0.000195215,0.00005706899,0.0002475723,0.00001412094,0.0001533285,0.0001009605,0.0001301308,0.00000665246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002268869,"about_ca_system_score_gemma":0.00002268142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004040979,"about_ca_topic_score_gemma":6.447748e-7,"domain_scores_codex":[0.9991406,0.00001212104,0.0002212311,0.0002511198,0.0001185697,0.0002563868],"domain_scores_gemma":[0.9988062,0.0007004256,0.00004343994,0.0003053012,0.00005709637,0.00008753665],"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":[0.000001364209,0.00001534438,0.0001208858,0.0002292971,0.00001277053,9.573564e-7,0.0002699123,0.9873534,0.0008709629,0.004293816,0.000005048653,0.00682621],"study_design_scores_gemma":[0.000306193,0.00003937226,0.001471433,0.0001683618,0.000008883798,0.000003412907,0.000006390107,0.9958461,0.001457117,0.00006907389,0.0004116822,0.0002119972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09836296,0.0005461698,0.8998934,0.00001210379,0.0004721184,0.00006437897,7.623554e-7,0.0006294483,0.00001868349],"genre_scores_gemma":[0.8828655,0.000001436375,0.1170094,0.000005588762,0.00007546317,0.000007607541,0.000001448529,0.00001927159,0.00001426512],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7845026,"threshold_uncertainty_score":0.7960638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009724914243210791,"score_gpt":0.222079671203829,"score_spread":0.2123547569606182,"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."}}