{"id":"W2398919057","doi":"","title":"GeospatialRules: A Datalog+ RuleML Rulebase for Geospatial Reasoning.","year":2014,"lang":"en","type":"article","venue":"Rules and Rule Markup Languages for the Semantic Web","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Datalog; Computer science; Geospatial analysis; RuleML; Task (project management); Set (abstract data type); Fragment (logic); Programming language; Conjunctive query; XML; Information retrieval; World Wide Web; Markup language","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":[0.005995233,0.001722694,0.001652714,0.004365637,0.001351423,0.008014522,0.006238775,0.002254869,0.0218347],"category_scores_gemma":[0.01944003,0.00251276,0.002955162,0.003800174,0.001636845,0.007085014,0.004317873,0.004783378,0.01616242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001752446,"about_ca_system_score_gemma":0.00643067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01394105,"about_ca_topic_score_gemma":0.01561716,"domain_scores_codex":[0.9948243,0.0007597043,0.001203813,0.0007296058,0.002292545,0.0001901086],"domain_scores_gemma":[0.9896173,0.004101444,0.0008238371,0.002729708,0.002251319,0.0004763961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005599012,0.0009501569,0.002378762,0.003143522,0.0005412938,0.002106802,0.001177483,0.07380123,0.01064431,0.2148897,0.3568352,0.3329716],"study_design_scores_gemma":[0.0002603236,0.00006549752,0.000620287,0.000588028,0.0001555221,0.0006749636,0.0001921995,0.1503028,0.01344091,0.1046033,0.7288926,0.0002036007],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001664843,0.0005046015,0.8415169,0.0007969626,0.0004128825,0.001661083,0.04791104,0.09279161,0.01274015],"genre_scores_gemma":[0.02156075,0.001146963,0.8327754,0.001126529,0.000176359,0.00220011,0.1237599,0.009127625,0.008126412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0218347,"threshold_uncertainty_score":0.07304436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006195305034952877,"score_gpt":0.2364738115818276,"score_spread":0.2302785065468747,"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."}}