{"id":"W2742802284","doi":"","title":"Multi Agent/HLA Enterprise Interoperability (Short-Lived Ontology Based)","year":2009,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Interoperability; Ontology; Computer science; Enterprise integration; Enterprise modelling; Knowledge management; Semantic interoperability; Enterprise information system; Reuse; Upper ontology; Enterprise systems engineering; Cross-domain interoperability; Enterprise software; World Wide Web; Enterprise architecture; Software engineering; Engineering; Architecture","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":[0.003643571,0.0002909703,0.000541504,0.0006552472,0.0009253933,0.00354618,0.001349213,0.001388089,0.006907197],"category_scores_gemma":[0.004549864,0.0003039249,0.0005952229,0.0008576133,0.0009158991,0.004185232,0.004292903,0.001803714,0.00236117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009399035,"about_ca_system_score_gemma":0.001556992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001771978,"about_ca_topic_score_gemma":0.001027787,"domain_scores_codex":[0.9981827,0.0006252516,0.000207053,0.0002060252,0.0005467575,0.0002323209],"domain_scores_gemma":[0.9974216,0.000416157,0.000165769,0.001295355,0.0004497744,0.0002513256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006627674,0.0005477249,0.001508352,0.0003827647,0.000105673,0.0009910548,0.001174939,0.02300487,0.04753394,0.6136613,0.02251861,0.287908],"study_design_scores_gemma":[0.000191356,0.0002686192,0.001319212,0.0001800089,0.000101423,0.0007190555,0.0005816828,0.3578441,0.05828005,0.3401286,0.2402803,0.0001055128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01627193,0.0001887336,0.9616219,0.0006605248,0.0002291281,0.000234982,0.0002579217,0.004449151,0.01608565],"genre_scores_gemma":[0.5259431,0.0005123978,0.4418792,0.000590105,0.0001445654,0.0004950349,0.002127879,0.0008470499,0.02746066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006907197,"threshold_uncertainty_score":0.02310687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03332758849631609,"score_gpt":0.2637758881235795,"score_spread":0.2304482996272635,"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."}}