{"id":"W1985442638","doi":"10.1117/12.718411","title":"Perception and mobility research at Defence R&D Canada for UGVs in complex terrain","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","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":"Terrain; Mobile robot; Computer science; Perception; Robot; Human–computer interaction; Artificial intelligence; Intelligent transportation system; Intelligent decision support system; Computer security; Engineering; Transport engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001621883,0.0007063726,0.0004357176,0.001452582,0.002389325,0.002859828,0.000951542,0.0009065419,0.008067649],"category_scores_gemma":[0.001821705,0.0002979729,0.0003863042,0.001697231,0.001713541,0.001655441,0.001281104,0.001876932,0.001488528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01816853,"about_ca_system_score_gemma":0.02195958,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6794884,"about_ca_topic_score_gemma":0.6097624,"domain_scores_codex":[0.9986503,0.0001400049,0.0000164144,0.000209703,0.0007150189,0.0002685856],"domain_scores_gemma":[0.9979068,0.00018021,0.0000550789,0.00008718484,0.001309843,0.0004608855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003130469,0.0002154559,0.01011311,0.0003761339,0.0000620443,0.0003322584,0.002483907,0.01473755,0.01726394,0.1718473,0.2670317,0.5152236],"study_design_scores_gemma":[0.00006941707,0.0002164399,0.01591357,0.0003273183,0.00005247782,0.0002286802,0.003279529,0.07528558,0.01843939,0.02617179,0.8598762,0.0001397444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.125803,0.06598587,0.2230164,0.150551,0.006238316,0.0004820329,0.003142797,0.002968141,0.4218124],"genre_scores_gemma":[0.605422,0.03707873,0.08242226,0.002896803,0.000582917,0.0001316027,0.002010866,0.0003551954,0.2690995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6794884,"threshold_uncertainty_score":0.6447983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03012569986627045,"score_gpt":0.2725419409299858,"score_spread":0.2424162410637154,"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."}}