{"id":"W2566867607","doi":"10.1109/crv.2016.33","title":"Automating Node Pruning for LiDAR-Based Topometric Maps in the Context of Teach-and-Repeat","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Pruning; Computer science; Context (archaeology); Node (physics); Dijkstra's algorithm; Artificial intelligence; Shortest path problem; Geography; Theoretical computer science","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":[0.0007512895,0.001042028,0.001156832,0.00187489,0.001132473,0.001250359,0.001953763,0.0008396572,0.001476803],"category_scores_gemma":[0.005652386,0.000528627,0.0006893858,0.001757328,0.0006606823,0.001921859,0.001687173,0.0009083746,0.00110409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005795153,"about_ca_system_score_gemma":0.001533138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006229806,"about_ca_topic_score_gemma":0.01855574,"domain_scores_codex":[0.999121,0.0001514675,0.00004079135,0.0002152603,0.0003700391,0.0001015023],"domain_scores_gemma":[0.997555,0.00105281,0.0003116546,0.0006201829,0.0003572715,0.0001032025],"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":[0.00033471,0.000223828,0.01065291,0.0003905242,0.0001303521,0.0004144639,0.000937583,0.207926,0.0453735,0.01087686,0.007925481,0.7148136],"study_design_scores_gemma":[0.00003519789,0.0001159358,0.003877443,0.00003823465,0.00004308129,0.0004626809,0.0005681188,0.9402052,0.03066703,0.0162868,0.007659275,0.00004107334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05247457,0.0002177378,0.9425688,0.0001593516,0.00003725967,0.0001238296,0.00032221,0.003055035,0.001041142],"genre_scores_gemma":[0.2760428,0.0001633336,0.7205006,0.00004806118,0.00003104318,0.0001429735,0.001346143,0.0004202348,0.001304823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006229806,"threshold_uncertainty_score":0.0123871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388969429438403,"score_gpt":0.2215603712559286,"score_spread":0.2076706769615446,"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."}}