{"id":"W2710002000","doi":"","title":"REVERSE SUBDIVISION FOR OPTIMIZING VISIBILITY TESTS","year":2018,"lang":"en","type":"article","venue":"International Conference on Computer Graphics Theory and Applications","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Terrain; Visibility; Computer science; Subdivision; Computation; Set (abstract data type); Computer vision; Algorithm; Artificial intelligence; Geography","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":[0.0007533974,0.0006601014,0.0005580665,0.0007287772,0.0004410261,0.0007443712,0.001021204,0.0005450947,0.002305158],"category_scores_gemma":[0.007275861,0.0003344479,0.0004217789,0.0006227838,0.0004806135,0.001042342,0.0009915904,0.0006303492,0.0004654557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005975643,"about_ca_system_score_gemma":0.000801202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00464417,"about_ca_topic_score_gemma":0.005570913,"domain_scores_codex":[0.9991966,0.0001237189,0.00005080543,0.0001077078,0.0004132584,0.0001079174],"domain_scores_gemma":[0.9969282,0.001567774,0.0002075382,0.0005788772,0.0006468832,0.00007087139],"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":[0.0004843494,0.0002267308,0.00606044,0.0002136426,0.00005122496,0.0001639862,0.0004093858,0.4062465,0.1253851,0.01158187,0.002668406,0.4465085],"study_design_scores_gemma":[0.0000222088,0.00008543116,0.0007655043,0.000008764033,0.00001237427,0.00007800623,0.00004517565,0.9579808,0.03705172,0.002029789,0.001906241,0.00001393854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.117679,0.0001761666,0.8757805,0.00006711952,0.00004582395,0.0001075002,0.00007989904,0.002239364,0.00382468],"genre_scores_gemma":[0.502052,0.00006585252,0.4960676,0.00003054353,0.000009357188,0.00007622223,0.0002540552,0.0004710693,0.0009732548],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00464417,"threshold_uncertainty_score":0.00923425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.027284051415511,"score_gpt":0.3038126417219419,"score_spread":0.276528590306431,"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."}}