{"id":"W2162174586","doi":"10.1109/crv.2012.10","title":"Probabilistic Obstacle Detection Using 2 1/2 D Terrain Maps","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Obstacle; Terrain; Computer science; Artificial intelligence; Probabilistic logic; Computer vision; Tree traversal; Representation (politics); Obstacle avoidance; Stereopsis; Task (project management); Range (aeronautics); Mobile robot; Robot; Geography; Engineering; Cartography","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":[0.0004172551,0.000440626,0.0003825099,0.001908193,0.0002874167,0.000898033,0.0006728746,0.0003581837,0.0006309427],"category_scores_gemma":[0.002553981,0.0004214039,0.0004975418,0.001154304,0.0005026268,0.0009746992,0.001190697,0.0003325206,0.0002641061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003731981,"about_ca_system_score_gemma":0.0005575794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003220451,"about_ca_topic_score_gemma":0.00396935,"domain_scores_codex":[0.9995733,0.00008207597,0.00001445134,0.00006808907,0.000216585,0.00004548966],"domain_scores_gemma":[0.9991652,0.0004732032,0.0001273642,0.00006854381,0.0001352872,0.00003047298],"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":[0.0001982755,0.0000428112,0.005041733,0.0001077876,0.00006245929,0.0002961876,0.0001977317,0.7029034,0.02617135,0.01418355,0.001101426,0.2496934],"study_design_scores_gemma":[0.00000654927,0.00002448011,0.001931568,0.000006688131,0.000008842447,0.00008650056,0.00002499641,0.9883105,0.003889659,0.004924383,0.0007696029,0.00001631674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04272204,0.0001006536,0.9555747,0.00004757437,0.000009115177,0.0000207214,0.00006421741,0.0006255108,0.0008355165],"genre_scores_gemma":[0.7742195,0.0002429532,0.2243827,0.00003446097,0.00002333363,0.00005701313,0.0002231582,0.00007812164,0.0007388293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003220451,"threshold_uncertainty_score":0.006403387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187519775453495,"score_gpt":0.2154113647747332,"score_spread":0.1966593872293837,"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."}}