{"id":"W1980440040","doi":"10.1021/acs.energyfuels.5b02472","title":"Selection of Optimal Solvent Type for High-Temperature Solvent Applications in Heavy-Oil and Bitumen Recovery","year":2016,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Statoil; Canadian Natural Resources Limited","keywords":"Asphaltene; Solvent; Mixing (physics); Chemistry; Diffusion; Yield (engineering); Precipitation; Asphalt; Viscosity; Chemical engineering; Chromatography; Materials science; Organic chemistry; Thermodynamics; Composite material","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":[0.00006816226,0.0001200394,0.0001665109,0.0001113202,0.00002438993,0.00001040649,0.00006972902,0.0001050898,0.00001562734],"category_scores_gemma":[0.00001307227,0.00009991935,0.00003628001,0.0001740761,0.00001880153,0.0001344644,0.0000182525,0.0000556571,0.00000101879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073127,"about_ca_system_score_gemma":0.00002062421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002659527,"about_ca_topic_score_gemma":0.0001564855,"domain_scores_codex":[0.9993839,0.000010969,0.0001983075,0.0001696941,0.00006728656,0.0001698303],"domain_scores_gemma":[0.999669,0.00007659374,0.00003695597,0.000131844,0.00004985135,0.00003573874],"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":[0.00004424712,0.00003732706,0.00016968,0.00008144658,0.00003235168,2.691384e-7,0.00002318668,0.001038897,0.9509434,0.002297339,0.0008736711,0.04445816],"study_design_scores_gemma":[0.0002265704,0.000139058,0.000521108,0.00008300316,0.000008211457,0.000001332665,0.000004878063,0.00005845536,0.9873812,0.0018055,0.00963256,0.0001381684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713444,0.0005191959,0.02709787,0.00007153235,0.0001567944,0.00009890777,0.00003550207,0.0002252087,0.0004506322],"genre_scores_gemma":[0.9870734,0.002152442,0.009525635,0.00001804125,0.00008154412,0.0003124659,0.00002329717,0.00003644509,0.0007767697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04431999,"threshold_uncertainty_score":0.4074593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005649370980947218,"score_gpt":0.2159557392700933,"score_spread":0.210306368289146,"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."}}