{"id":"W3034000815","doi":"10.2118/0620-0067-jpt","title":"Advancing Production Flow Profiling With Subatomic Fingerprints and Big Data Analytics","year":2020,"lang":"en","type":"article","venue":"Journal of Petroleum Technology","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydraulic fracturing; Analytics; Big data; Completion (oil and gas wells); Computer science; Subatomic particle; Profiling (computer programming); Oil shale; Process engineering; Petroleum engineering; Environmental science; Engineering; Data science; Operating system; Waste management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002380002,0.0001237591,0.0002550808,0.0003351113,0.00003828285,0.00001824082,0.0002760015,0.00009582503,0.000002629983],"category_scores_gemma":[0.0002030259,0.0001034146,0.00001895245,0.0003521889,0.00006789843,0.0002472263,0.00009089975,0.000497241,0.000001527881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004469571,"about_ca_system_score_gemma":0.00003588842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.044752e-7,"about_ca_topic_score_gemma":0.000003846759,"domain_scores_codex":[0.9991751,0.00001023961,0.0003113119,0.0001985342,0.0001303069,0.0001745258],"domain_scores_gemma":[0.9993973,0.000007667988,0.0001421651,0.0002910407,0.00009716041,0.00006469515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001811632,0.00006678575,0.02644417,0.0004799863,0.0003588769,0.0001412779,0.0002073213,0.03441705,0.1691104,0.00007221368,0.003390945,0.7651299],"study_design_scores_gemma":[0.000961439,0.001065108,0.001156708,0.0002565952,0.0002381808,0.002218451,0.0008095086,0.1077324,0.8661099,0.001345571,0.01755494,0.0005511511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570346,0.0009728417,0.03355154,0.007368984,0.0004375446,0.00006969654,0.000006066327,0.0005014216,0.00005722827],"genre_scores_gemma":[0.9571836,0.0005816635,0.04169788,0.00002988154,0.0004679719,0.00000164107,0.000002173985,0.00002556114,0.000009647844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7645787,"threshold_uncertainty_score":0.4217123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109458467517943,"score_gpt":0.2235617156186955,"score_spread":0.2024671309435161,"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."}}