{"id":"W1484555300","doi":"10.1002/rob.21490","title":"Mapping, Planning, and Sample Detection Strategies for Autonomous Exploration","year":2013,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sample (material); Computer science; Fidelity; Field (mathematics); Robot; Global Positioning System; Simultaneous localization and mapping; Object detection; Real-time computing; Autonomous robot; Object (grammar); Artificial intelligence; High fidelity; Computer vision; Mobile robot; Engineering; Pattern recognition (psychology); Mathematics","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":[0.0005775474,0.0006083153,0.0005591814,0.000425666,0.0004362227,0.000599188,0.001069707,0.0006012371,0.001352054],"category_scores_gemma":[0.001913291,0.0003874606,0.0003343417,0.0003484539,0.0009381093,0.001183418,0.001313026,0.0006685925,0.0002055757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004464749,"about_ca_system_score_gemma":0.001059999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003304506,"about_ca_topic_score_gemma":0.003090346,"domain_scores_codex":[0.9996673,0.00009313791,0.00001683842,0.0000794862,0.00009786484,0.00004541283],"domain_scores_gemma":[0.9991162,0.0004899158,0.0000996216,0.0001258402,0.0001149118,0.00005344421],"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":[0.0002357554,0.0001747973,0.001226471,0.0001187565,0.00005759952,0.0001100659,0.000312085,0.806802,0.01526769,0.02014774,0.001221058,0.154326],"study_design_scores_gemma":[0.00003278612,0.00008947944,0.0002191625,0.000004478655,0.000008332136,0.00002606882,0.0000458445,0.9897268,0.002363215,0.006888319,0.0005876213,0.000007733986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08684614,0.0001437189,0.9104849,0.0001462546,0.00001683734,0.00007258844,0.00002363653,0.0005176574,0.001748169],"genre_scores_gemma":[0.7531405,0.00008481282,0.245178,0.00004829019,0.00001243379,0.0001698601,0.00005110243,0.00006054296,0.001254434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003304506,"threshold_uncertainty_score":0.006570578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465289206731626,"score_gpt":0.235421632647954,"score_spread":0.2107687405806377,"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."}}