{"id":"W2061778301","doi":"10.1117/12.669556","title":"Intelligent mobility research for robotic locomotion in complex terrain","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"McGill University","keywords":"Computer science; Robotics; Mobile robot; Terrain; Intelligent decision support system; Robot; Artificial intelligence; Human–computer interaction; Intelligent transportation system; Active perception; Engineering; Transport engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002818038,0.0002972873,0.0004559886,0.0002623785,0.0001246588,0.0002107491,0.002068769,0.0001984122,0.00000358759],"category_scores_gemma":[0.000853332,0.0002647366,0.0004652656,0.000767358,0.0002891934,0.000653933,0.0003664383,0.0004480492,0.000002083005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004291734,"about_ca_system_score_gemma":0.00005802788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006956112,"about_ca_topic_score_gemma":7.362161e-7,"domain_scores_codex":[0.9967375,6.949931e-8,0.0009343322,0.0006399887,0.001007356,0.0006807874],"domain_scores_gemma":[0.9968998,0.0004705165,0.0003032253,0.000123301,0.002094471,0.0001086339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005323614,0.0004843726,0.0008738522,0.0005843392,0.0001134514,2.419021e-7,0.0004363451,0.01154237,0.1907679,0.787962,0.006321542,0.0008603676],"study_design_scores_gemma":[0.001023088,0.0004440422,0.00697391,0.0003131228,0.00002762798,0.0000153035,0.0005759193,0.9367145,0.03273493,0.01963655,0.001177473,0.0003635146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9657223,0.00006126847,0.02703872,0.004576521,0.0002625412,0.001316221,0.0000162987,0.0001147981,0.0008912899],"genre_scores_gemma":[0.3869912,0.00001218307,0.6121047,0.00004738557,0.0003180856,0.000338574,0.00001447844,0.00003807991,0.0001352852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9251722,"threshold_uncertainty_score":0.9999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04343404558250471,"score_gpt":0.2990645385593622,"score_spread":0.2556304929768575,"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."}}