{"id":"W2286218711","doi":"10.11575/prism/31050","title":"Visualizing highly multidimensional time varying Microseismic Events","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures; ConocoPhillips","keywords":"Microseism; Computer science; Visualization; Interactive visual analysis; Rendering (computer graphics); Visual analytics; Data visualization; Data mining; Set (abstract data type); Domain (mathematical analysis); Data science; Filter (signal processing); Outlier; Process (computing); Interactive visualization; Anomaly detection; Data set; Curse of dimensionality; Machine learning; Artificial intelligence; Computer vision; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002664085,0.0001056686,0.0001028002,0.00009129611,0.000124162,0.00005601478,0.0003568374,0.00003655341,0.00015747],"category_scores_gemma":[0.00002519136,0.00009286799,0.00004522795,0.0002643928,0.0000129429,0.0009642003,0.0003192347,0.00005237489,0.002174521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002697997,"about_ca_system_score_gemma":0.00002605977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001220949,"about_ca_topic_score_gemma":1.432381e-7,"domain_scores_codex":[0.9990188,0.00004516669,0.0001878892,0.0001932061,0.0002385575,0.0003163967],"domain_scores_gemma":[0.9994081,0.00005023865,0.00005700306,0.0002860412,0.00004625881,0.0001524005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008894773,0.001236969,0.01336959,0.00004364329,0.0001246787,0.00001124917,0.001974247,0.0002743061,0.1811446,0.707662,0.07795613,0.01619371],"study_design_scores_gemma":[0.0008082908,0.00003468257,0.002946417,0.00004641646,0.00001615438,0.00004086093,0.0000194104,0.808769,0.03588366,0.0005414787,0.1502883,0.0006053027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02707909,0.00009672929,0.9676102,0.0004433973,0.000481503,0.00008501786,0.000005732079,0.0003466357,0.003851671],"genre_scores_gemma":[0.8650525,0.00001067943,0.1225798,0.004351857,0.0001563346,0.000002688576,0.00005711976,0.00001730528,0.007771753],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8450304,"threshold_uncertainty_score":0.9986024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02330704079865313,"score_gpt":0.3019470958313597,"score_spread":0.2786400550327066,"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."}}