{"id":"W2057530735","doi":"10.1021/ef100765u","title":"Recovery of Bitumen from Utah Tar Sands Using Ionic Liquids","year":2010,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Oil sands; Asphalt; Slurry; Mixing (physics); Asphaltene; Toluene; tar (computing); Ionic liquid; Solvent; Chemical engineering; Viscosity; Environmental science; Geology; Mineralogy; Chemistry; Materials science; Organic chemistry; Environmental engineering; Composite material; Catalysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000156644,0.0004529214,0.0002432899,0.0005803489,0.0004135413,0.0004057006,0.000355789,0.0002291077,0.001464932],"category_scores_gemma":[0.0002958702,0.000137973,0.0001911845,0.0004243321,0.0002426989,0.0002894403,0.0004981571,0.0004068214,0.000687825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003834369,"about_ca_system_score_gemma":0.0008107434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008138693,"about_ca_topic_score_gemma":0.02960056,"domain_scores_codex":[0.999835,0.00001044961,0.00001214776,0.00002777246,0.00007708446,0.00003740716],"domain_scores_gemma":[0.9998971,0.00001628918,0.00001827478,0.0000132046,0.00003959135,0.00001556593],"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":[0.00003287397,0.000009735772,0.0005063416,0.00002777712,0.000004932306,0.00005504897,0.00004040194,0.0001434027,0.9945919,0.00004873172,0.00006594438,0.004472825],"study_design_scores_gemma":[0.000002135918,0.00002700575,0.001746669,0.000004991608,0.000006017149,0.00002812549,0.00003549561,0.0004200531,0.996278,0.00001507342,0.001431747,0.000004643648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840768,0.000534263,0.01029943,0.00008357725,0.0000221737,0.00007408137,0.0004082285,0.000315484,0.00418597],"genre_scores_gemma":[0.9681102,0.000953305,0.01970544,0.00006417076,0.000009029385,0.00004495642,0.001126429,0.0001113145,0.009875236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008138693,"threshold_uncertainty_score":0.0161826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009430557265494842,"score_gpt":0.2300678141454645,"score_spread":0.2206372568799697,"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."}}