{"id":"W2060927936","doi":"10.1021/ie301949c","title":"Thermodynamic Investigation of Asphaltene Precipitation during Primary Oil Production: Laboratory and Smart Technique","year":2013,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"","keywords":"Asphaltene; Bubble point; Thermodynamics; Precipitation; Imperialist competitive algorithm; Enhanced oil recovery; Scaling; Crude oil; Artificial neural network; Petroleum engineering; Chemistry; Bubble; Environmental science; Materials science; Computer science; Mathematics; Geology; Algorithm; Physics; Machine learning; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002202449,0.0002592067,0.0003233548,0.0003054197,0.0001908019,0.0001917649,0.000320059,0.0002767319,0.0004300477],"category_scores_gemma":[0.0003985761,0.000144091,0.0002379788,0.0003473205,0.0003031832,0.0003531712,0.0002515815,0.0003800088,0.0001401368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00015337,"about_ca_system_score_gemma":0.0001927112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007435094,"about_ca_topic_score_gemma":0.0009677763,"domain_scores_codex":[0.9998449,0.00001875293,0.000007243445,0.00003743815,0.00008018142,0.00001136603],"domain_scores_gemma":[0.9998326,0.00005549483,0.00003078724,0.00002570977,0.00004499461,0.00001036113],"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":[0.0001184343,0.0001163549,0.003159657,0.0001223585,0.000009277916,0.0001114303,0.00009671915,0.003870454,0.9788727,0.0002989029,0.0000754987,0.01314819],"study_design_scores_gemma":[0.00001062803,0.0003728551,0.006465497,0.000004723571,0.00001930731,0.0001481251,0.00008157883,0.08597258,0.9061235,0.0001712988,0.0006114373,0.00001843765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502957,0.0003310897,0.04717456,0.00007110505,0.00002405043,0.00004488536,0.0002285568,0.0001943184,0.001635657],"genre_scores_gemma":[0.989255,0.0001849891,0.01005552,0.00001051721,0.000008277976,0.00002411848,0.00006952648,0.00001068063,0.0003813575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007435094,"threshold_uncertainty_score":0.001478374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02656229691510103,"score_gpt":0.2603644528873648,"score_spread":0.2338021559722637,"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."}}