{"id":"W2760600361","doi":"10.20944/preprints201709.0126.v1","title":"Overview of the OGC CDB Standard for 3D Synthetic Environment Simulation & Modeling","year":2017,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interoperability; Geospatial analysis; Computer science; Variety (cybernetics); Database; Sensor web; Reusability; Data modeling; Systems engineering; Terrain; Software engineering; World Wide Web; Remote sensing; Engineering; Geography; Telecommunications; Cartography; Operating system; Software","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005755186,0.001908474,0.001245849,0.007291321,0.001482684,0.009130446,0.003917707,0.003163793,0.01616754],"category_scores_gemma":[0.009096051,0.001345241,0.001650876,0.008514925,0.001414041,0.005644707,0.004605073,0.004837033,0.02418626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003313969,"about_ca_system_score_gemma":0.00703673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02507388,"about_ca_topic_score_gemma":0.01248305,"domain_scores_codex":[0.9941923,0.000806468,0.0009439517,0.0005469241,0.003112515,0.0003977783],"domain_scores_gemma":[0.9945908,0.0006199059,0.000288319,0.001199322,0.002824007,0.0004776886],"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":[0.0002333686,0.0002377965,0.002395754,0.001611064,0.00009185863,0.0005215961,0.000697227,0.02727514,0.010096,0.2339787,0.3367433,0.3861182],"study_design_scores_gemma":[0.00001259208,0.00001379131,0.0002690164,0.0003266252,0.00001447462,0.0001483111,0.00005571893,0.009683703,0.001384182,0.00807736,0.9799703,0.00004384495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00218617,0.009708031,0.8406346,0.002964548,0.002091515,0.001670408,0.01638751,0.02476742,0.09958977],"genre_scores_gemma":[0.02682878,0.02932995,0.7547434,0.003402629,0.001032457,0.002976634,0.1118217,0.01227066,0.05759382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02507388,"threshold_uncertainty_score":0.05408585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2264486540946443,"score_gpt":0.3676114489208771,"score_spread":0.1411627948262328,"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."}}