{"id":"W2040208164","doi":"10.1115/ipc2014-33675","title":"A Novel Approach for Automatic Matching and Updating Pipeline Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Pipeline (software); Robustness (evolution); Geographic information system; Pipeline transport; Data mining; Multidisciplinary approach; Data transformation; Matching (statistics); Upgrade; Data science; Spatial analysis; Transformation (genetics); Database; Engineering; Data warehouse","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":[0.001143957,0.001220534,0.001422312,0.004003603,0.001247295,0.001992073,0.003306749,0.001708635,0.00342378],"category_scores_gemma":[0.003428594,0.0009311248,0.00140444,0.00588487,0.0006544861,0.003342872,0.002797369,0.001496008,0.003790309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006631808,"about_ca_system_score_gemma":0.001861764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009049986,"about_ca_topic_score_gemma":0.01212782,"domain_scores_codex":[0.997221,0.0001818151,0.0001839677,0.0010689,0.0011592,0.0001850503],"domain_scores_gemma":[0.9981609,0.0002241569,0.0001599686,0.0006274412,0.0007625745,0.00006503808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001445769,0.0001881829,0.002092532,0.0001189609,0.0001005627,0.0001658585,0.0001840026,0.01101516,0.04407096,0.003432687,0.009951986,0.9285345],"study_design_scores_gemma":[0.00007028844,0.0001766502,0.004443407,0.00002873692,0.0001137316,0.001311183,0.0003322223,0.8527774,0.0777708,0.007844634,0.05501037,0.000120527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005265279,0.00008324059,0.9881579,0.00007179149,0.0000763879,0.0001017487,0.000295421,0.005284017,0.0006641067],"genre_scores_gemma":[0.03183593,0.00009807204,0.9637846,0.00008375014,0.00003629189,0.0001110263,0.001501486,0.0003136462,0.002235153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009049986,"threshold_uncertainty_score":0.01799458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06586039674667764,"score_gpt":0.325066374394566,"score_spread":0.2592059776478884,"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."}}