{"id":"W2066125425","doi":"10.1002/cjce.5450780418","title":"Predicting pressure gradients in heavy oil—water pipelines","year":2000,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Research Council (Canada)","funders":"","keywords":"Pressure gradient; Pipeline transport; Asphalt; Petroleum engineering; Flow (mathematics); Pressure drop; Oil field; Oil well; Mechanics; Environmental science; Fraction (chemistry); Geology; Materials science; Chemistry; Environmental engineering; Chromatography; Composite material; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001821727,0.0001130324,0.0001878371,0.00008952549,0.00005179492,0.00005817402,0.0003001279,0.00007840487,0.0004622966],"category_scores_gemma":[0.00008396304,0.00007520083,0.00009002258,0.0001128387,0.00003218306,0.000086085,0.00000638249,0.0004595879,0.000004509896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008504591,"about_ca_system_score_gemma":0.00009397128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001363309,"about_ca_topic_score_gemma":0.0002945983,"domain_scores_codex":[0.9991117,0.000005290279,0.0003118383,0.00008548505,0.0001480634,0.0003376383],"domain_scores_gemma":[0.9995024,0.00002743059,0.00004391919,0.0001157934,0.00004133774,0.0002690805],"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":[0.00007428547,0.00004444026,0.01284239,0.000421774,0.0003337332,0.0002884624,0.002458791,0.202364,0.7389664,0.00001328851,0.0002873044,0.04190516],"study_design_scores_gemma":[0.001348913,0.00001529133,0.0001517278,0.001362649,0.0002722412,0.0005616748,0.00006854402,0.1176059,0.8315917,0.00008251608,0.04638179,0.0005570347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968028,0.0008625039,0.000003487629,0.0004619981,0.00003918286,0.000001623368,0.000004073972,0.00001162306,0.001812704],"genre_scores_gemma":[0.9978347,0.000006563425,0.00005361541,0.00003603455,0.0003301007,0.000001042112,0.000002378346,0.0000166453,0.001718895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09262529,"threshold_uncertainty_score":0.5061824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00482302401677091,"score_gpt":0.18032301906232,"score_spread":0.1754999950455491,"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."}}