{"id":"W2282857338","doi":"10.4271/2000-01-1917","title":"Fuel Lubricity: Statistical Analysis of Literature Data","year":2000,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shell Canada; Southwest Research Institute","keywords":"Lubricity; Statistical analysis; Forensic engineering; Computer science; Econometrics; Statistics; Engineering; Mathematics; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02095668,0.001256224,0.004470559,0.04570709,0.0006557201,0.003285807,0.001909366,0.0009220985,0.02274811],"category_scores_gemma":[0.09140107,0.000575481,0.005090952,0.03706029,0.001106606,0.002699947,0.002969563,0.002267787,0.005259981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374068,"about_ca_system_score_gemma":0.003030916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070188,"about_ca_topic_score_gemma":0.001360692,"domain_scores_codex":[0.9724965,0.006582097,0.006263257,0.003993828,0.009560004,0.001104321],"domain_scores_gemma":[0.8611826,0.1064874,0.0134882,0.006530465,0.01132964,0.0009816575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01347666,0.002170613,0.2465213,0.07525717,0.01487256,0.001998655,0.003603802,0.006940289,0.009553584,0.005130273,0.1126871,0.5077879],"study_design_scores_gemma":[0.001366055,0.009675073,0.5628508,0.00891945,0.009946619,0.003206434,0.009750002,0.03213044,0.01307669,0.01425735,0.3340127,0.000808314],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3217292,0.01255938,0.07226475,0.001572832,0.0006343592,0.02268935,0.5393779,0.009559033,0.01961326],"genre_scores_gemma":[0.5344323,0.006634999,0.1673984,0.0008438392,0.0005871261,0.1199986,0.1571474,0.003539372,0.009417941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9542929,"threshold_uncertainty_score":0.1108308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421089671380463,"score_gpt":0.264856391033049,"score_spread":0.2506454943192444,"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."}}