{"id":"W6964241543","doi":"10.25318/2510006701-eng","title":"Canadian oil pipeline carriers, monthly operating statistics","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Pipeline transport; Product (mathematics); Petroleum; Statistical analysis; Tonne","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009663486,0.002098073,0.001346205,0.01159448,0.001941298,0.003228357,0.002413449,0.0009363902,0.07431332],"category_scores_gemma":[0.01109615,0.001253418,0.0010933,0.04128076,0.0004435564,0.001595671,0.001141597,0.001873479,0.03716208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02971866,"about_ca_system_score_gemma":0.07255744,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9894992,"about_ca_topic_score_gemma":0.9879907,"domain_scores_codex":[0.9973132,0.00009772578,0.0002691564,0.0003277063,0.001399216,0.0005929994],"domain_scores_gemma":[0.9884998,0.0004907961,0.0006166638,0.0004105764,0.00944269,0.0005394805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001700374,0.000007760308,0.001260971,0.0002144627,0.00001308101,0.00001074763,0.00001812298,0.0001976773,0.00001123092,0.0006032574,0.9953547,0.00229108],"study_design_scores_gemma":[0.00006829816,0.000009553736,0.0390246,0.0003000912,0.00003813682,0.00002958211,0.0002126973,0.0004961384,0.0001704222,0.0004631227,0.9591429,0.00004451346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000154223,0.00007253296,0.00003220664,0.0000840951,0.00001828474,0.00001307253,0.9974988,0.00005239886,0.002074291],"genre_scores_gemma":[0.001953497,0.0004530514,0.000302088,0.00006748985,0.0000137971,0.00007760059,0.986272,0.00006999893,0.01079052],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07431332,"threshold_uncertainty_score":0.2486028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007489678212089173,"score_gpt":0.2606539671611693,"score_spread":0.2531642889490802,"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."}}