{"id":"W2112299333","doi":"10.1007/3-540-44842-x_76","title":"An Efficient Algorithm for Real-Time 3D Terrain Walkthrough","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Terrain; Software walkthrough; Rendering (computer graphics); Computer graphics (images); Visualization; Raised-relief map; Algorithm; Terrain rendering; Representation (politics); Real-time rendering; Computer vision; Artificial intelligence; Software","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":[0.000433583,0.001753823,0.001203247,0.001463732,0.0007727966,0.001518866,0.002312497,0.001238995,0.01665404],"category_scores_gemma":[0.001387983,0.0008563513,0.0008945023,0.001631338,0.0005074977,0.001389811,0.002326842,0.00117245,0.004374569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007495668,"about_ca_system_score_gemma":0.001099428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007186626,"about_ca_topic_score_gemma":0.01242716,"domain_scores_codex":[0.9994379,0.00004834615,0.00003317033,0.000108387,0.0003038855,0.00006837956],"domain_scores_gemma":[0.9995178,0.0001564595,0.0000281093,0.00009515804,0.000161405,0.00004098852],"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":[0.0003007709,0.0001304102,0.0004087218,0.000192264,0.00005263229,0.0001247469,0.0001959499,0.06200482,0.02819398,0.01249117,0.01182409,0.8840804],"study_design_scores_gemma":[0.0001197772,0.00009561168,0.0003020856,0.00002680896,0.00003204649,0.0002171341,0.00006867984,0.9578273,0.01595075,0.008505208,0.0168197,0.00003500124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002500559,0.00008293647,0.9927504,0.00002635478,0.00003539611,0.00005241214,0.00006566498,0.003279272,0.001206893],"genre_scores_gemma":[0.03375991,0.0001123895,0.9623763,0.00002765133,0.00001347064,0.0001206665,0.0003617908,0.0003965485,0.00283118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01665404,"threshold_uncertainty_score":0.05571336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505993426672987,"score_gpt":0.2874284161871382,"score_spread":0.2723684819204084,"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."}}