{"id":"W2074440952","doi":"10.1115/ipc2004-0362","title":"Spatially Enabled Pipeline Route Optimization Model","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Computer science; Pipeline transport; Hazard; Risk analysis (engineering); Process (computing); Routing (electronic design 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":[0.001023676,0.0008754853,0.0008077641,0.001134349,0.0007032623,0.002261634,0.002164693,0.002239428,0.009551605],"category_scores_gemma":[0.0014257,0.0006376654,0.001238617,0.001534638,0.0009212681,0.001844503,0.001562351,0.001358079,0.0007806795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002561265,"about_ca_system_score_gemma":0.002817919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02162587,"about_ca_topic_score_gemma":0.01446507,"domain_scores_codex":[0.9991423,0.000299631,0.00003976383,0.0001881477,0.0002061401,0.0001239965],"domain_scores_gemma":[0.9994235,0.0002757997,0.00009289876,0.00003324738,0.0001288493,0.00004580373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001342492,0.00001297606,0.0001420204,0.00002564338,0.000008658972,0.00006249663,0.00002272866,0.9738057,0.0002576614,0.02242676,0.0003400312,0.002881841],"study_design_scores_gemma":[0.000008608718,0.00001615567,0.00007409789,0.000006084628,0.000007441851,0.00001613853,0.00002178844,0.9900985,0.000121911,0.007929645,0.001691453,0.000008055997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02258269,0.0002865469,0.9432819,0.0008831422,0.00009078826,0.0001728853,0.001149873,0.000403345,0.0311488],"genre_scores_gemma":[0.7560969,0.001036069,0.1982962,0.0002008164,0.00006687322,0.00073938,0.0009065766,0.0001332363,0.04252394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02162587,"threshold_uncertainty_score":0.04299998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338469445398051,"score_gpt":0.2174036850021744,"score_spread":0.2040189905481939,"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."}}