{"id":"W2748382447","doi":"","title":"Binary Classification for Hydraulic Fracturing Operations in Oil & GasWells via Tree Based Logistic RBF Networks","year":2017,"lang":"en","type":"article","venue":"European Journal of Pure and Applied Mathematics","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Logistic regression; Logistic model tree; Generalization; Radial basis function; Data mining; Mathematics; Artificial neural network; Data set; Artificial intelligence; Computer science; Statistics","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":[0.001210223,0.0005954811,0.0006653227,0.0008394775,0.0003014742,0.0007497218,0.001110521,0.0009498663,0.001335503],"category_scores_gemma":[0.003451324,0.0002493048,0.000739887,0.0009777914,0.0003669126,0.001638054,0.0006827195,0.0009121634,0.0004318904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008116478,"about_ca_system_score_gemma":0.000441384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005179966,"about_ca_topic_score_gemma":0.004650907,"domain_scores_codex":[0.9994265,0.0001651312,0.00003835859,0.0001658828,0.0001479597,0.00005620974],"domain_scores_gemma":[0.9989522,0.0004798089,0.0001815948,0.00005846238,0.0002885892,0.00003935579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003100522,0.0001206518,0.01101524,0.0002234109,0.0000732624,0.0001825337,0.0001417753,0.6368524,0.00619672,0.007254804,0.002432962,0.3351962],"study_design_scores_gemma":[0.000002323208,0.00001277724,0.0007534278,0.000004850826,0.000004520381,0.00001924375,0.000009945091,0.9969217,0.0005728393,0.001452718,0.0002391972,0.000006480324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08354311,0.000672054,0.9128824,0.0003666396,0.0000634833,0.00003897978,0.0001674977,0.0006221325,0.001643622],"genre_scores_gemma":[0.9007072,0.0004149322,0.09509698,0.00008421359,0.0000717471,0.00005476115,0.0002844516,0.00005496217,0.003230737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005179966,"threshold_uncertainty_score":0.01029962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723386929494944,"score_gpt":0.2453817454921145,"score_spread":0.2181478761971651,"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."}}