{"id":"W2543408643","doi":"10.1109/iat.2005.50","title":"Category-based Similarity Algorithm for Semantic Similarity in Multi-agent Information Sharing Systems","year":2006,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Intelligent Agent Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Semantic similarity; Similarity (geometry); Computer science; Cosine similarity; Information retrieval; Vector space model; Matching (statistics); Similarity measure; Data mining; Artificial intelligence; Pattern recognition (psychology); Mathematics; Image (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":[0.003299111,0.0006433942,0.001566627,0.003418806,0.001507323,0.002151127,0.002471033,0.002035216,0.002989938],"category_scores_gemma":[0.01274465,0.0003041458,0.0008950961,0.004110811,0.001256684,0.004439041,0.002486819,0.0014993,0.000963691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00211972,"about_ca_system_score_gemma":0.002022442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003667155,"about_ca_topic_score_gemma":0.002309268,"domain_scores_codex":[0.9952609,0.00164085,0.0004648428,0.0007329433,0.001692695,0.0002078343],"domain_scores_gemma":[0.9957525,0.001824508,0.0002693564,0.0004793876,0.001528117,0.0001462605],"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":[0.0002308787,0.0002482588,0.002024681,0.0004032252,0.0001823155,0.0001790967,0.0005763708,0.1987039,0.003947543,0.2939823,0.008125667,0.4913957],"study_design_scores_gemma":[0.00003971227,0.0001147429,0.0003741859,0.00003595182,0.00002770455,0.0001632407,0.0001300208,0.8427029,0.001880934,0.1442772,0.01021844,0.00003506167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0039862,0.0003421538,0.993112,0.0001207074,0.00006683837,0.0001671242,0.00005004287,0.0003034494,0.001851504],"genre_scores_gemma":[0.1931332,0.0003464732,0.802703,0.0001387332,0.00009509669,0.0007040099,0.0003557901,0.00009597955,0.002427704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003667155,"threshold_uncertainty_score":0.01744759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08304876257932589,"score_gpt":0.3153846540817681,"score_spread":0.2323358915024422,"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."}}