{"id":"W2099559152","doi":"10.1109/iros.2009.5354670","title":"Mobile manipulation using tracks of a tracked mobile robot","year":2009,"lang":"en","type":"article","venue":"","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mobile robot; Computer science; Robot; Mobile manipulator; Acceleration; Point (geometry); Artificial intelligence; Simulation; Computer vision","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":[0.00006133509,0.00009777948,0.0001414084,0.00005659908,0.00002093782,0.00001229708,0.00008023022,0.00006072799,0.0002984903],"category_scores_gemma":[0.000002495665,0.00009147201,0.00005435086,0.0001177606,0.00001071287,0.0001106413,0.00000409638,0.00006473621,0.00001295265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002523242,"about_ca_system_score_gemma":0.000004854401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001914378,"about_ca_topic_score_gemma":0.000003841345,"domain_scores_codex":[0.999406,0.000007196958,0.000235037,0.0001037838,0.0001056441,0.00014235],"domain_scores_gemma":[0.9997364,0.000008129163,0.00002275442,0.000165108,0.00002946216,0.00003818757],"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":[0.000002522003,0.00003124269,0.00004150403,0.00001640012,0.000006814306,0.000001622309,0.0002364891,0.875361,0.0726665,0.0006779667,0.00003105022,0.05092691],"study_design_scores_gemma":[0.0001084937,0.0001493711,0.001091659,0.00002251471,0.00001350921,0.000009493938,0.0001222604,0.8217793,0.1755013,0.0007401636,0.0002786576,0.0001832594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5036489,0.00029734,0.4925286,0.000001485611,0.0000852576,0.0001972046,0.000001123571,0.0001245623,0.00311545],"genre_scores_gemma":[0.9893361,0.00003784314,0.01043337,0.00001398841,0.00003416796,0.000006987149,0.000003807401,0.00001281983,0.0001209516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4856871,"threshold_uncertainty_score":0.373012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669356647184517,"score_gpt":0.2649944488942967,"score_spread":0.2383008824224515,"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."}}