{"id":"W1984422408","doi":"10.1115/ipc2006-10472","title":"Managing GIS and Spatial Data to Support Effective Decision Making Throughout the Pipeline Lifecycle","year":2006,"lang":"en","type":"article","venue":"Volume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations","topic":"Marine and Offshore Engineering Studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Geographic information system; Pipeline (software); Computer science; Nuclear decommissioning; Data science; Component (thermodynamics); Decision support system; Spatial analysis; Data warehouse; Engineering; Data mining; Geography; Cartography","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":[0.01645318,0.001505294,0.001472158,0.008427661,0.002626294,0.01478803,0.004551496,0.002453163,0.01195717],"category_scores_gemma":[0.04310708,0.00158334,0.00132338,0.009756072,0.002442728,0.02119265,0.01162201,0.003876223,0.008632908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002997755,"about_ca_system_score_gemma":0.008911439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01124663,"about_ca_topic_score_gemma":0.01079919,"domain_scores_codex":[0.9873292,0.003993995,0.001445997,0.00141727,0.005124969,0.0006884558],"domain_scores_gemma":[0.9544053,0.0151911,0.003538372,0.009710736,0.0140581,0.003096376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000164457,0.0003470137,0.013667,0.001328647,0.0003491848,0.001459093,0.007638217,0.05123229,0.007010737,0.08751309,0.219173,0.6101173],"study_design_scores_gemma":[0.000049874,0.0001113991,0.004911843,0.0007783169,0.0001523596,0.0007564864,0.01326265,0.06200739,0.006855142,0.1368552,0.7739525,0.0003068623],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02401647,0.00336658,0.8155883,0.0454645,0.001215178,0.001389463,0.00724951,0.01693978,0.08477023],"genre_scores_gemma":[0.1591471,0.004578209,0.7983488,0.003387654,0.0005777943,0.000717106,0.01578318,0.003249283,0.01421091],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01645318,"threshold_uncertainty_score":0.08701384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286727307213614,"score_gpt":0.2678452668720239,"score_spread":0.2549779937998877,"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."}}