{"id":"W2093514844","doi":"10.2118/04-05-tn3","title":"Low Field NMR Water Cut Metering","year":2004,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Canadian Natural Resources","funders":"University of Calgary","keywords":"Hydraulic fracturing; Petroleum engineering; Fracture (geology); Mechanics; Finite element method; Fluid dynamics; Metering mode; Geology; Geotechnical engineering; Flow (mathematics); Extraction (chemistry); Stress (linguistics); Engineering; Mechanical engineering; Structural engineering; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002540333,0.0003486154,0.0001696608,0.0005711736,0.000302432,0.000380164,0.0004113839,0.0004771682,0.009759067],"category_scores_gemma":[0.0003496117,0.0001291796,0.00008227812,0.0003673305,0.0002445103,0.0004762302,0.0002944292,0.0003442209,0.001829092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004046512,"about_ca_system_score_gemma":0.0002203907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008626021,"about_ca_topic_score_gemma":0.001859012,"domain_scores_codex":[0.9998066,0.00003064875,0.000005679228,0.00003937537,0.00009972932,0.00001790596],"domain_scores_gemma":[0.9998099,0.00004498238,0.00001830418,0.00002675582,0.00008415878,0.00001581509],"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":[0.0004339964,0.0001092375,0.005585301,0.0002858519,0.00001802086,0.0001637974,0.0001353403,0.003802166,0.8115398,0.002603035,0.01062074,0.1647028],"study_design_scores_gemma":[0.00006116421,0.0002934426,0.01065048,0.00004512625,0.00003867531,0.0001876579,0.0001339197,0.06387211,0.8902211,0.001285626,0.0331506,0.00006031111],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4287535,0.0009692044,0.443204,0.001350414,0.0007050661,0.0004205511,0.005061618,0.009192728,0.1103428],"genre_scores_gemma":[0.9054155,0.0002609841,0.07292208,0.0004571363,0.00006522905,0.0001170564,0.0007879243,0.0001502091,0.01982377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009759067,"threshold_uncertainty_score":0.03264731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004714664766879009,"score_gpt":0.2512641107312108,"score_spread":0.2465494459643318,"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."}}