{"id":"W3191181181","doi":"10.1061/9780784483602.010","title":"Increasing Pipelines’ Resilience for a Changing Climate","year":2021,"lang":"en","type":"article","venue":"Pipelines 2021","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Pharmacists Association","funders":"","keywords":"Pipeline transport; Sustainability; Resilience (materials science); Extreme weather; Environmental science; Natural disaster; Environmental resource management; Risk analysis (engineering); Population; Climate change; Environmental planning; Natural resource economics; Environmental economics; Business; Engineering; Civil engineering; Environmental 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000369646,0.0001447549,0.0001933569,0.0000945751,0.0001319641,0.00008914803,0.00008546677,0.00006779046,0.00005389392],"category_scores_gemma":[0.0001575698,0.0001465888,0.00007320559,0.000370008,0.000009493293,0.0001522833,0.00006272521,0.00006033498,0.0000373948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003101945,"about_ca_system_score_gemma":0.00002068007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001350957,"about_ca_topic_score_gemma":0.0001212374,"domain_scores_codex":[0.9988699,0.00002866825,0.0002960913,0.0002201241,0.0001090695,0.0004761682],"domain_scores_gemma":[0.9994229,0.0000737686,0.00003210668,0.0002221637,0.000189092,0.00005994734],"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":[0.0000894023,0.00009964238,0.008677214,0.002282537,0.0001090777,0.0001440677,0.002756837,0.858982,0.06660132,0.002130548,0.01711259,0.04101477],"study_design_scores_gemma":[0.0005014647,0.00001038969,0.0002655329,0.000268624,0.00003157937,0.00008428319,0.0006366383,0.9474304,0.0206509,0.0000796796,0.02967541,0.0003651135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1296798,0.004302196,0.8552628,0.0004133128,0.003158604,0.0004753762,0.00008603907,0.0005000903,0.006121813],"genre_scores_gemma":[0.9683409,0.0004087031,0.02737812,0.00009513894,0.001586659,0.00007140232,0.0001524051,0.00007415612,0.001892474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8386611,"threshold_uncertainty_score":0.5977716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009400674867236882,"score_gpt":0.2211477991503256,"score_spread":0.2117471242830887,"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."}}