{"id":"W2073630622","doi":"10.1081/lft-120018170","title":"Particle Characterization for Oil Sand Processing. III. Sample Preparation","year":2003,"lang":"en","type":"article","venue":"Petroleum Science and Technology","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Syncrude (Canada)","funders":"Syncrude","keywords":"Particle size; Repeatability; Particle (ecology); Oil sands; Mineralogy; Characterization (materials science); Materials science; Sample preparation; Asphalt; Particle-size distribution; Analytical Chemistry (journal); Chemistry; Chromatography; Composite material; Geology; Nanotechnology","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":[0.00026432,0.00005790954,0.00007822718,0.0001527996,0.0001685628,0.00005284774,0.00007411283,0.00004029263,0.000006519258],"category_scores_gemma":[0.0001280496,0.00005380128,0.000009444779,0.0006237485,0.0001283798,0.0002385929,0.000007046136,0.00003290428,0.000002077591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000258043,"about_ca_system_score_gemma":0.00003220178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001147635,"about_ca_topic_score_gemma":0.000006515296,"domain_scores_codex":[0.9994484,0.00000265832,0.00009658152,0.000149229,0.0001161267,0.0001870669],"domain_scores_gemma":[0.9997626,0.000007160268,0.00002026929,0.00008891397,0.00009389546,0.00002713961],"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":[0.00000394664,0.00001680319,0.00497564,0.00003018959,0.000006157351,1.499399e-7,0.00008216262,0.0001802604,0.9020839,0.003029427,0.00001862987,0.08957274],"study_design_scores_gemma":[0.0005103269,0.00008229556,0.0005606118,0.00001461472,0.00002951605,0.000003932283,0.0001053701,0.4141334,0.5670029,0.001141991,0.01622257,0.0001924793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734492,0.0002100483,0.02521215,0.0002276178,0.00005738255,0.00003660829,0.000002258119,0.0002069561,0.0005977185],"genre_scores_gemma":[0.9991089,0.00004067738,0.0006836872,0.00001423521,0.00001141618,0.00004389169,0.000003668216,0.000004852636,0.00008871331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4139532,"threshold_uncertainty_score":0.2193952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104501877279024,"score_gpt":0.2243092211202423,"score_spread":0.2138590333923399,"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."}}