{"id":"W2089741835","doi":"10.2118/163834-ms","title":"Utilizing Hybrid Surface - Downhole Seismic Networks to Monitor Hydraulic Fracture Stimulations","year":2013,"lang":"en","type":"article","venue":"SPE Hydraulic Fracturing Technology Conference","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Apheresis Group","funders":"","keywords":"Geophone; Microseism; Induced seismicity; Wireline; Passive seismic; Seismology; Seismometer; Geology; Accelerometer; Hydraulic fracturing; Range (aeronautics); Engineering; Geotechnical engineering; Computer science; Telecommunications","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":[0.0001457711,0.0002732359,0.0002134727,0.0008741706,0.0001243097,0.0002181287,0.0002867231,0.0002323153,0.0006803231],"category_scores_gemma":[0.0003226293,0.0001414209,0.0000888456,0.0005656637,0.0001582537,0.0003183132,0.0004158879,0.0001580777,0.00009209562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002516845,"about_ca_system_score_gemma":0.0001740529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003941238,"about_ca_topic_score_gemma":0.01585899,"domain_scores_codex":[0.9998258,0.0000286939,0.000005778768,0.00004905693,0.00006468961,0.00002608895],"domain_scores_gemma":[0.9997497,0.00005426056,0.00007122525,0.00001652727,0.00007033593,0.00003794332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009130364,0.0003056532,0.2981524,0.0001369714,0.0001110381,0.0004761159,0.0007303316,0.02259308,0.5409761,0.000211232,0.00056834,0.1348258],"study_design_scores_gemma":[0.00008149705,0.001293704,0.7479112,0.0000243788,0.0001155167,0.0004954304,0.0006989985,0.1770745,0.07022293,0.0002937431,0.001719626,0.00006866614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931016,0.00002768829,0.005930407,0.00001557043,0.000005709815,0.00002045805,0.0001783008,0.00009580959,0.0006244938],"genre_scores_gemma":[0.9940963,0.000018669,0.00549774,0.000006795662,0.000005716096,0.00001361746,0.0001084768,0.000006099736,0.0002465861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003941238,"threshold_uncertainty_score":0.00783658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204523734667507,"score_gpt":0.220769512661691,"score_spread":0.2087242753150159,"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."}}