{"id":"W1542110664","doi":"10.1002/9781118989982.ch5","title":"Chromatographic Fingerprinting Analysis of Crude Oils and Petroleum Products","year":2014,"lang":"en","type":"other","venue":"","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Petroleum; Identification (biology); Biochemical engineering; Environmental science; Oil spill; Crude oil; Petroleum engineering; Chromatographic separation; Computer science; Chemistry; Engineering; Chromatography","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":[0.000341121,0.0007720808,0.0004188636,0.002690511,0.0003962094,0.0006458576,0.0004934982,0.0004476791,0.004565466],"category_scores_gemma":[0.0004747615,0.0001712019,0.0003730801,0.001786821,0.0002582624,0.0008629885,0.0005168444,0.0004010058,0.003423753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382063,"about_ca_system_score_gemma":0.0005378033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001995874,"about_ca_topic_score_gemma":0.002857177,"domain_scores_codex":[0.9995387,0.00002446234,0.00001736055,0.00008286654,0.000285358,0.00005129657],"domain_scores_gemma":[0.9998592,0.00002657255,0.00002757759,0.00001314919,0.00006434511,0.000009163115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001507857,0.0001017133,0.004882806,0.001463351,0.00005385066,0.0005775536,0.0001881855,0.001595021,0.4795838,0.006096408,0.005991449,0.4993151],"study_design_scores_gemma":[0.000007276192,0.0002006873,0.01224516,0.0002770121,0.00007563348,0.002348862,0.000296149,0.005384799,0.7343693,0.002613669,0.2421164,0.00006501925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2848208,0.1101984,0.3463041,0.001070681,0.0008047848,0.0006830657,0.01066597,0.003906782,0.2415455],"genre_scores_gemma":[0.5109404,0.1330394,0.2098261,0.0009173253,0.0003692834,0.0003014975,0.007771316,0.0007531366,0.1360815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004565466,"threshold_uncertainty_score":0.01527303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006247939864966359,"score_gpt":0.2199779048950001,"score_spread":0.2137299650300338,"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."}}