{"id":"W2891052268","doi":"10.5194/isprs-annals-iv-4-13-2018","title":"NETWORK MODELLING AND SEMANTIC 3D CITY MODELS: TESTING THE MATURITY OF THE UTILITY NETWORK ADE FOR CITYGML WITH A WATER NETWORK TEST CASE","year":2018,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Verkehr, Innovation und Technologie; European Commission","keywords":"CityGML; Computer science; Semi-structured model; Relational database; Network element; Network model; Data mining; Database model; Database; Visualization; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003388311,0.0005612337,0.000335427,0.0009412882,0.0007407379,0.002329667,0.002065922,0.00120716,0.004909553],"category_scores_gemma":[0.01081063,0.0004071277,0.0008616857,0.001353739,0.001460822,0.00297773,0.00217315,0.00109336,0.0007230296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002932355,"about_ca_system_score_gemma":0.001769692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07859126,"about_ca_topic_score_gemma":0.07609075,"domain_scores_codex":[0.9980707,0.0008176036,0.0001198452,0.0002282189,0.0006497545,0.0001139867],"domain_scores_gemma":[0.994193,0.003246163,0.0001828158,0.001304355,0.0008878476,0.0001857801],"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":[0.0005575593,0.0006411964,0.01810564,0.0003187298,0.00008558977,0.000465826,0.001234921,0.8734351,0.004165987,0.04983056,0.006094036,0.04506477],"study_design_scores_gemma":[0.00008060448,0.0001176913,0.002446166,0.00004955609,0.00001755329,0.00006092317,0.0007291306,0.9732527,0.004956882,0.005058289,0.01319705,0.00003340085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8259585,0.0001284148,0.1204429,0.00115539,0.0001135255,0.0006878334,0.007847832,0.005193742,0.03847187],"genre_scores_gemma":[0.8479815,0.0001219962,0.1411156,0.0001168996,0.000009228688,0.0002898344,0.006797514,0.0006508637,0.002916624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07859126,"threshold_uncertainty_score":0.1562676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0821217239599083,"score_gpt":0.2674640896783635,"score_spread":0.1853423657184552,"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."}}