{"id":"W2806851634","doi":"10.5555/3213200.3213207","title":"Data mining classifiers comparison for seismic hazard prediction","year":2018,"lang":"en","type":"article","venue":"Communications and Networking Symposium","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Feature selection; Computer science; Data mining; Confusion matrix; Random forest; Data pre-processing; Resampling; Seismic hazard; Artificial intelligence; Seismology; Geology","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":[0.004732653,0.001045425,0.001523385,0.00342058,0.0007073848,0.00130686,0.0009387585,0.001216677,0.001867134],"category_scores_gemma":[0.01113947,0.0002029705,0.001351605,0.00199714,0.0001968167,0.001122759,0.000545692,0.001028996,0.0006580863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008650235,"about_ca_system_score_gemma":0.00111156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004914065,"about_ca_topic_score_gemma":0.003117076,"domain_scores_codex":[0.997426,0.0005088077,0.0005199481,0.0003604269,0.0009169725,0.0002678394],"domain_scores_gemma":[0.9926518,0.003833135,0.000299953,0.0003669606,0.002627987,0.0002201922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00300335,0.001134298,0.05082011,0.0009815122,0.0006918827,0.0003326004,0.0002852688,0.1002184,0.005250103,0.002057172,0.01753491,0.8176904],"study_design_scores_gemma":[0.00019208,0.002061369,0.0335515,0.0003629391,0.0006438378,0.0005326603,0.0007315299,0.9220956,0.02169732,0.004208209,0.01383915,0.00008375749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7178848,0.02047734,0.2244711,0.00284572,0.002733286,0.001274531,0.006561274,0.003364154,0.02038782],"genre_scores_gemma":[0.8977422,0.003078406,0.08987011,0.0002814251,0.0002954563,0.0005012167,0.005243777,0.00006117219,0.002926275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004914065,"threshold_uncertainty_score":0.02502894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1274306002769645,"score_gpt":0.3354082191831009,"score_spread":0.2079776189061364,"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."}}