{"id":"W1994996129","doi":"10.1016/j.cageo.2012.09.016","title":"Efficient occlusion-free visualization for navigation in mountainous areas","year":2012,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Terrain; Computer science; Computer vision; Visibility; Visualization; Perspective (graphical); Occlusion; Artificial intelligence; Projection (relational algebra); Tracing; Frame (networking); Computer graphics (images); Cartography; Geography; Algorithm","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":[0.0002293059,0.0009098449,0.0008060295,0.0008502553,0.0006071829,0.001120796,0.0007802957,0.0005183067,0.004973302],"category_scores_gemma":[0.00130881,0.0004377396,0.0004788746,0.001039863,0.0002198048,0.0008767227,0.001416017,0.0008624607,0.0006651005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002769703,"about_ca_system_score_gemma":0.0008390397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00715112,"about_ca_topic_score_gemma":0.01439752,"domain_scores_codex":[0.9997805,0.00003535931,0.000007967451,0.000023548,0.0001128313,0.00003976783],"domain_scores_gemma":[0.9996114,0.0001431117,0.00002874661,0.00006872599,0.0001105578,0.00003742338],"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":[0.001156799,0.0002650401,0.003774483,0.0005041737,0.0001334593,0.0005486634,0.0009564161,0.1348424,0.1317862,0.01352567,0.03151814,0.6809886],"study_design_scores_gemma":[0.00009094394,0.00009897002,0.002374927,0.00003914697,0.00004738622,0.0002800768,0.0001437006,0.9488384,0.02527284,0.008100215,0.01467167,0.00004176093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08319376,0.0006245541,0.9044875,0.0002279035,0.00009885142,0.00005933176,0.0006305352,0.006420712,0.004256742],"genre_scores_gemma":[0.4342937,0.0008184455,0.5564687,0.00008006105,0.00007444399,0.0001027936,0.001370853,0.001064659,0.005726357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00715112,"threshold_uncertainty_score":0.01663733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824819214540349,"score_gpt":0.3026907028561762,"score_spread":0.2844425107107728,"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."}}