{"id":"W2751177463","doi":"","title":"Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Object-Relational Logic.","year":2017,"lang":"en","type":"article","venue":"Rules and Rule Markup Languages for the Semantic Web","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"RuleML; Computer science; Object (grammar); Port (circuit theory); Relational database; Programming language; Database; Artificial intelligence; XHTML; Markup language; XML; World Wide Web; Engineering","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":[0.0007839426,0.0002490554,0.0004691899,0.000109552,0.0006830219,0.0005232078,0.001063725,0.000134651,0.00003944928],"category_scores_gemma":[0.0007817285,0.0001667326,0.0001401404,0.00006358984,0.0001484966,0.0004239833,0.0003820731,0.0001529066,0.00004264891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002773782,"about_ca_system_score_gemma":0.00005402798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006620564,"about_ca_topic_score_gemma":0.0005829957,"domain_scores_codex":[0.9982731,0.00007779245,0.000398945,0.0005389355,0.0002999573,0.0004112871],"domain_scores_gemma":[0.997754,0.0008328705,0.0002574142,0.000963282,0.0001096445,0.0000827593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001996271,0.0004143068,0.0561936,0.0002478654,0.0006548063,0.0001868155,0.01014536,0.0009805395,0.002546896,0.7069859,0.01825159,0.2013961],"study_design_scores_gemma":[0.003995276,0.00008641271,0.8973292,0.0001825952,0.00006922,0.00001424323,0.000621047,0.05778593,0.0001405838,0.03451934,0.004819483,0.0004367064],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453383,0.005423254,0.03086753,0.008200474,0.001018273,0.00143612,0.0000884142,0.000222657,0.007404984],"genre_scores_gemma":[0.9810138,0.0002715231,0.01659369,0.0004358412,0.0004086407,0.00008345369,0.00002652971,0.00001875,0.001147766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8411356,"threshold_uncertainty_score":0.679916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595183694955659,"score_gpt":0.2659193856058454,"score_spread":0.2499675486562888,"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."}}