{"id":"W2111799146","doi":"10.5539/mas.v5n4p158","title":"Geovisualization of Sub-surface Pipelines: A 3D Approach","year":2011,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline transport; Damages; Computer science; Pipeline (software); Visualization; Geographic information system; Witness; Construction engineering; Risk analysis (engineering); Mining engineering; Civil engineering; Geology; Environmental science; Engineering; Remote sensing; Business; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002207837,0.00009984782,0.0001679694,0.0001645367,0.0007858338,0.00004721363,0.0005430336,0.00005836459,0.00001816839],"category_scores_gemma":[0.0000828494,0.00009168372,0.00003374383,0.001491826,0.001572428,0.0004116307,0.0001070708,0.00005711186,0.00003298669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004666517,"about_ca_system_score_gemma":0.0002031409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007974292,"about_ca_topic_score_gemma":0.00009512392,"domain_scores_codex":[0.9980485,0.00002884128,0.0003463324,0.0002555985,0.0009550064,0.0003657323],"domain_scores_gemma":[0.9990153,0.0000257382,0.0002375662,0.0002432163,0.0003878049,0.00009035996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002557073,0.0001819539,0.01134576,0.00008140787,0.00001697358,2.639132e-7,0.5451868,0.0005687162,0.03239654,0.4051114,0.0001412208,0.004943344],"study_design_scores_gemma":[0.003112698,0.0002237771,0.06778166,0.0001696501,0.0001419146,0.000008810854,0.3836018,0.3847889,0.06100119,0.08553545,0.0104254,0.003208709],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1736578,0.00004495483,0.3113767,0.00001893354,0.0001551994,0.0004980837,0.000002774974,0.0001167578,0.5141287],"genre_scores_gemma":[0.9927335,0.00002039073,0.006979998,0.00004948601,0.00002623656,0.00002201303,0.000001237158,0.000005244918,0.0001618556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8190757,"threshold_uncertainty_score":0.6044078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05487566322840034,"score_gpt":0.2840945826575672,"score_spread":0.2292189194291669,"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."}}