{"id":"W1978227679","doi":"10.1016/j.fuel.2013.08.006","title":"A practical method for the separation of high quality heavy oil and bitumen samples from oil reservoir cores for physical and chemical property determination","year":2013,"lang":"en","type":"article","venue":"Fuel","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Calgary","keywords":"Asphalt; Physical property; Environmental science; Petroleum engineering; Separation (statistics); Quality (philosophy); Chemistry; Materials science; Geology; Computer science; Organic chemistry; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002387271,0.0000959438,0.000210826,0.00002758485,0.00005361149,0.00005508279,0.00005594544,0.00006642537,0.000007746328],"category_scores_gemma":[0.0003711228,0.00005510724,0.00005362032,0.00006727006,0.00005240268,0.0002115113,0.00002516905,0.00006581543,0.000001142947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000154712,"about_ca_system_score_gemma":0.00001400562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004577653,"about_ca_topic_score_gemma":0.000165868,"domain_scores_codex":[0.9993408,0.0000612822,0.0002069022,0.0001588487,0.0001245974,0.0001076113],"domain_scores_gemma":[0.9986134,0.001039672,0.00005197612,0.0001443344,0.000100799,0.00004984747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008055266,0.0004292189,0.0003947684,0.002436748,0.0007964312,6.29519e-7,0.007830932,0.005058679,0.6067306,0.004004984,0.007587973,0.3639235],"study_design_scores_gemma":[0.0005469342,0.0000503135,0.0006373418,0.00001505587,0.00009107362,6.90819e-7,0.0002350685,0.9646702,0.02833655,0.003276102,0.00202672,0.0001138819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7920784,0.0001977978,0.2023726,0.004649279,0.00006213158,0.0002463895,0.00009870201,0.00007124109,0.0002235026],"genre_scores_gemma":[0.9523849,0.0001068527,0.04662368,0.00003870737,0.0001366872,0.0003729954,0.0000779674,0.00001554075,0.0002425902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9596116,"threshold_uncertainty_score":0.2247208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05534326178518027,"score_gpt":0.3492329162259107,"score_spread":0.2938896544407305,"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."}}