{"id":"W2043678458","doi":"10.2523/iptc-14720-ms","title":"Use of Nano-Metal Particles as Catalyst Under Electromagnetic Heating for Viscosity Reduction of Heavy Oil","year":2011,"lang":"en","type":"article","venue":"International Petroleum Technology Conference","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Catalysis; Hydrogen; Materials science; Viscosity; Metal; Asphalt; Chemical engineering; Solvent; Nano-; Enhanced oil recovery; Sulfur; Carbon fibers; Chemistry; Composite material; Metallurgy; 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":[0.0001040016,0.0001544417,0.0002978941,0.0002978173,0.00006642334,0.00001982196,0.0004183844,0.0001775853,0.0002236854],"category_scores_gemma":[0.0002403771,0.0001566758,0.0001264108,0.0001860548,0.0003334255,0.0001843602,0.00007989704,0.0001707179,0.000004911014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005859617,"about_ca_system_score_gemma":0.0001217176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004808894,"about_ca_topic_score_gemma":0.00005168851,"domain_scores_codex":[0.9987602,0.00001196735,0.0004600601,0.0003214575,0.0002216814,0.0002246189],"domain_scores_gemma":[0.9988078,0.0000524612,0.000395064,0.0002802905,0.0004275981,0.00003682024],"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":[0.0001418099,0.0002112219,0.002928618,0.00005691155,0.0002163194,0.000001273169,0.00007212012,0.00004058421,0.9747546,0.009844804,0.000008054947,0.01172373],"study_design_scores_gemma":[0.0003832045,0.0001391844,0.0002214873,0.0001178019,0.0001117757,0.00004251124,0.0003713711,0.003915657,0.9892992,0.005081122,0.0001729588,0.0001437737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940951,0.0001068709,0.001549049,0.0004224135,0.0000636269,0.000007070871,0.00003906138,0.0001012589,0.003615554],"genre_scores_gemma":[0.9937959,0.00003751981,0.003387896,0.00001122308,0.00002855381,0.00003548735,0.0000368225,0.00001375584,0.002652871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0145446,"threshold_uncertainty_score":0.6389053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0343055786292475,"score_gpt":0.2622187405502633,"score_spread":0.2279131619210158,"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."}}