{"id":"W2563903707","doi":"10.1504/ijipt.2016.081318","title":"K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns","year":2016,"lang":"en","type":"article","venue":"International Journal of Internet Protocol Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Complex event processing; Leverage (statistics); SQL; Data mining; Event (particle physics); Database; Machine learning; Process (computing)","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":[0.0007098083,0.0002245679,0.0002913371,0.001305438,0.00003425915,0.0002465995,0.003927744,0.000122829,0.0001511847],"category_scores_gemma":[0.0003246138,0.0001562098,0.0001285993,0.000438081,0.00010116,0.0008342598,0.0009341703,0.0002745937,0.00009623398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002657767,"about_ca_system_score_gemma":0.00009478943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001056937,"about_ca_topic_score_gemma":0.00001510505,"domain_scores_codex":[0.9977701,0.0001198161,0.000846981,0.000376749,0.0005942071,0.0002920941],"domain_scores_gemma":[0.9977363,0.0001340898,0.0007642196,0.00037912,0.0008818309,0.0001043955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000232852,0.0006251132,0.00225954,0.00007392278,0.0002226915,0.0002966937,0.002229348,0.00002201938,0.000726165,0.07567618,0.004823526,0.9128119],"study_design_scores_gemma":[0.01221846,0.005715519,0.004904923,0.01183988,0.00004445536,0.0004312767,0.001485126,0.04121958,0.04806913,0.1713122,0.7008039,0.001955512],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001891387,0.000004871969,0.9669518,0.01818032,0.0004269148,0.01207349,0.00001370158,0.0001320306,0.0003254912],"genre_scores_gemma":[0.7794378,9.09149e-7,0.2031335,0.0006799151,0.0002635098,0.01596651,0.000002872135,0.00002322028,0.0004916823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9108564,"threshold_uncertainty_score":0.7298792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04094910362408077,"score_gpt":0.406556510535927,"score_spread":0.3656074069118462,"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."}}