{"id":"W3170802253","doi":"10.1016/j.petrol.2021.109089","title":"Efficient tracking and estimation of solvent chamber development during warm solvent injection in heterogeneous reservoirs via machine learning","year":2021,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Solvent; Process engineering; Computer science; Tracking (education); Oil shale; Petroleum engineering; Asphalt; Environmental science; Simulation; Materials science; Chemistry; Geology; Waste management; Engineering; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179418,0.0001356631,0.0002444384,0.0005293649,0.00008044105,0.00005554411,0.00007087366,0.00004643072,0.000002977823],"category_scores_gemma":[0.0002141187,0.0001353505,0.00003692417,0.0004477408,0.00002947916,0.0001909225,0.00004994471,0.000293652,1.867597e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001905601,"about_ca_system_score_gemma":0.0000501658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004044361,"about_ca_topic_score_gemma":0.000004333073,"domain_scores_codex":[0.9986625,0.00001919102,0.0005049775,0.0001392861,0.0004245504,0.0002495287],"domain_scores_gemma":[0.9995261,0.00005629478,0.00008896214,0.00008046146,0.0001334974,0.0001146565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006672645,0.00001220282,0.0009694779,0.000160759,0.00001169495,0.00002652182,0.0006150373,0.8383544,0.1581354,0.000003064613,1.040657e-7,0.001704627],"study_design_scores_gemma":[0.0003978292,0.00003681877,0.01734884,0.0002155732,0.000006629182,0.000313747,0.00006884986,0.9117489,0.06971242,0.000003185968,0.00003131671,0.0001158342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9138346,0.001278748,0.08459063,0.00001268132,0.0002164714,0.00002868304,2.705603e-7,0.00002628772,0.00001165307],"genre_scores_gemma":[0.9839963,0.0001185588,0.01582767,0.00000114425,0.00003145044,0.000001816039,4.932606e-7,0.0000165331,0.000006086152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08842302,"threshold_uncertainty_score":0.5519432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093067004298763,"score_gpt":0.2417974431785272,"score_spread":0.2308667731355395,"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."}}