{"id":"W2900122902","doi":"10.1115/ipc2018-78289","title":"Combining Expert Knowledge and Automation to Maximize Pipeline Route Optionality and Defensibility: A Case Study of the Aurora Pipeline","year":2018,"lang":"en","type":"article","venue":"","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada); Golder Associates (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Constraint (computer-aided design); Process (computing); Automation; Operations research; Engineering","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":[0.00567377,0.0006279889,0.0003865233,0.001063468,0.003539485,0.002867997,0.001567242,0.002513262,0.002903493],"category_scores_gemma":[0.01152861,0.0004385265,0.0005354091,0.001383202,0.002337095,0.002457065,0.001879202,0.001423134,0.0004534333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003671525,"about_ca_system_score_gemma":0.00351545,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03679353,"about_ca_topic_score_gemma":0.1080321,"domain_scores_codex":[0.9958432,0.002658433,0.0001507211,0.0002992284,0.0006141912,0.0004341369],"domain_scores_gemma":[0.988918,0.007549617,0.0004940014,0.0009270928,0.001437689,0.0006736908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"case_report","study_design_scores_codex":[0.00117753,0.005444787,0.06796886,0.001060692,0.0002126086,0.0513818,0.07294751,0.3927055,0.0104509,0.04890016,0.01650033,0.3312492],"study_design_scores_gemma":[0.0007565728,0.005819896,0.04211901,0.000480083,0.0002645898,0.009764121,0.1528233,0.5605783,0.02040483,0.04249248,0.1641264,0.0003703657],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9305894,0.0001450774,0.02833868,0.001691104,0.00002370542,0.0005023227,0.0001563832,0.0001732132,0.03838013],"genre_scores_gemma":[0.9468115,0.0001677739,0.04617196,0.0001377351,0.000009976978,0.00009519122,0.000120258,0.00006305402,0.006422624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9632065,"threshold_uncertainty_score":0.07315874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03120235540642756,"score_gpt":0.3243196333526672,"score_spread":0.2931172779462397,"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."}}