{"id":"W1972980611","doi":"10.2118/09-03-15-da","title":"Advances in Magnetic Resonance Relaxometry for Heavy Oil and Bitumen Characterization","year":2009,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canada Research Chairs; Canadian Natural Resources Limited","keywords":"Relaxometry; Characterization (materials science); Petroleum engineering; Asphalt; Computer science; Environmental science; Permeability (electromagnetism); Process engineering; Construction engineering; Geology; Nanotechnology; Materials science; Engineering; Chemistry; Magnetic resonance imaging","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008000183,0.00007182403,0.0001601641,0.001112447,0.00006088656,0.00001649239,0.0001236532,0.00005673909,0.00002068884],"category_scores_gemma":[0.000008729695,0.00007236107,0.00002756627,0.0005229751,0.00003848382,0.0001511216,0.000004356449,0.000179821,0.000001150215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005136281,"about_ca_system_score_gemma":0.0001212354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000278366,"about_ca_topic_score_gemma":0.001784952,"domain_scores_codex":[0.999413,0.000006480784,0.0002321835,0.00009706689,0.00004642905,0.0002048298],"domain_scores_gemma":[0.9996307,0.0000137126,0.0001331144,0.00009214829,0.00004357638,0.00008671744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002794326,0.00004263443,0.01980937,0.000005204817,0.000003579519,0.000004584377,0.00002393536,0.000005328809,0.01367519,0.1366119,0.0001301117,0.8296602],"study_design_scores_gemma":[0.001083634,0.0006366305,0.0390676,0.00007994069,0.00001656694,0.00003305625,0.0001349872,0.0001910162,0.004482727,0.05262724,0.9014689,0.0001777466],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561543,0.005833586,0.002543777,0.02872839,0.0001080811,0.00007661736,0.00006581799,0.00001580218,0.006473574],"genre_scores_gemma":[0.9971866,0.0006111338,0.001638696,0.000115683,0.0001075302,0.00001211635,0.000004504579,0.000006069462,0.0003176744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9013388,"threshold_uncertainty_score":0.2950799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003493767848994278,"score_gpt":0.2531383676068771,"score_spread":0.2496445997578828,"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."}}