{"id":"W2397532987","doi":"10.82308/55097","title":"Unsupervised learning for mobile robot terrain classification","year":2010,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Terrain; Artificial intelligence; Cluster analysis; Mobile robot; Computer science; Robot; Identification (biology); Computer vision; Tactile sensor; Robotics; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005135433,0.0003312301,0.0002862451,0.0001914221,0.000618418,0.00008790826,0.0002841929,0.0003485854,0.0001271719],"category_scores_gemma":[0.0003337468,0.0003648847,0.0001602331,0.0003264899,0.00004513688,0.0004042295,0.00003650579,0.0008163728,0.0001205643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001626492,"about_ca_system_score_gemma":0.00001120147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002405909,"about_ca_topic_score_gemma":0.0001135103,"domain_scores_codex":[0.9982101,0.00007922888,0.0004789277,0.0004553341,0.0002720501,0.0005043326],"domain_scores_gemma":[0.9988716,0.0001785454,0.00008665013,0.0004540992,0.0001907834,0.0002183641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001759262,0.00005420296,0.00008528667,0.00008969768,0.00003808641,0.000002468949,0.000007484353,0.2160448,0.6717786,0.02655793,0.00000751273,0.08531629],"study_design_scores_gemma":[0.0016928,0.0002329406,0.001630017,0.00006582754,0.00008809575,0.00001735217,0.0001797404,0.6450319,0.1648053,0.004649462,0.1804793,0.001127332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857022,0.00004277664,0.001351112,0.00002604485,0.001093484,0.0008648089,0.0001115834,0.0009492873,0.009858693],"genre_scores_gemma":[0.9930692,0.000035271,0.005703786,0.00008503049,0.0000808543,0.0002206485,0.0002280256,0.0001616051,0.0004156307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5069734,"threshold_uncertainty_score":0.9998803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597461193154043,"score_gpt":0.2233336997819461,"score_spread":0.2073590878504057,"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."}}