{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006584821,0.0005531678,0.0009275051,0.0008425853,0.000600581,0.0008430853,0.001716213,0.001146673,0.002797078],"category_scores_gemma":[0.002503892,0.0003816419,0.001034778,0.001300404,0.0005832626,0.001219185,0.001054304,0.001203349,0.001520065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007370081,"about_ca_system_score_gemma":0.001022732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008372821,"about_ca_topic_score_gemma":0.009767139,"domain_scores_codex":[0.999465,0.0001093389,0.00003873929,0.0001594118,0.0001398496,0.00008760475],"domain_scores_gemma":[0.9990012,0.000377201,0.0001126234,0.000215193,0.0002537044,0.00004005078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002142645,0.0001568039,0.003418563,0.00031582,0.0001327348,0.0001289361,0.0001470299,0.3589086,0.008393672,0.01136755,0.01095206,0.6058639],"study_design_scores_gemma":[0.00000966512,0.00002888068,0.000918334,0.00001655951,0.000009834491,0.00003999325,0.000034579,0.9838795,0.001765369,0.009733864,0.003550554,0.00001281895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02123582,0.001327875,0.9708635,0.000501633,0.0002137881,0.0001045651,0.0005113319,0.002306346,0.002935193],"genre_scores_gemma":[0.5886246,0.001371136,0.3898712,0.000467069,0.0004166868,0.0004003169,0.003422478,0.0004536219,0.01497292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008372821,"threshold_uncertainty_score":0.01664817,"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."}}