{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006488041,0.00006832407,0.00005674962,0.00004003586,0.00003819718,0.0000179487,0.00002528803,0.00004429688,0.00005787783],"category_scores_gemma":[0.00001839265,0.00006513226,0.00001961082,0.0001006826,0.0000086962,0.0001129568,0.000005392492,0.00004410569,0.00003271721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007420318,"about_ca_system_score_gemma":0.000002878603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002440356,"about_ca_topic_score_gemma":0.00001563251,"domain_scores_codex":[0.9995897,0.00001084393,0.0001015946,0.00005389178,0.00006444169,0.0001795408],"domain_scores_gemma":[0.9998151,0.00001320841,0.000008434531,0.0000919134,0.00001532706,0.00005600708],"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":[0.000001536286,0.00001918422,0.000616081,0.00004537618,0.000008997238,4.049049e-7,0.0001883262,0.9620684,0.03119434,0.001075898,0.00009451759,0.004686931],"study_design_scores_gemma":[0.0000819169,0.000007816299,0.0007398493,0.000006046286,0.000009462371,0.00000692267,0.00004558344,0.9840636,0.01352565,0.0001810766,0.001217206,0.0001149325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5166395,0.00005564527,0.4792585,0.000006434451,0.0004608863,0.00009914085,0.000001242643,0.0002512697,0.003227303],"genre_scores_gemma":[0.9970208,0.000001584874,0.002732377,0.00002016331,0.0001201705,0.000002446284,0.000005485164,0.00002060562,0.00007636243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4803813,"threshold_uncertainty_score":0.2656017,"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."}}