{"id":"W3103346840","doi":"","title":"Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":417,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bit (key); Computer science; Sampling (signal processing); Random graph; Algorithm; Theoretical computer science; Artificial intelligence; Graph; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003894864,0.0007160809,0.001268387,0.001441939,0.0002439887,0.00051316,0.005337812,0.0005279513,0.00002585923],"category_scores_gemma":[0.001838796,0.0005007166,0.000440552,0.002229065,0.000317961,0.0002201287,0.003018928,0.00146605,0.00003727909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002001331,"about_ca_system_score_gemma":0.002008501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007949866,"about_ca_topic_score_gemma":0.000005590417,"domain_scores_codex":[0.9938779,0.0003551109,0.001548562,0.0011497,0.001973556,0.001095151],"domain_scores_gemma":[0.9914746,0.003689488,0.0006439844,0.002627423,0.001122055,0.0004424523],"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":[0.0001110119,0.0001067781,0.002067785,0.0001651017,0.0001827626,0.00004381388,0.0008232704,0.9854096,0.00005680954,0.0007944627,0.004314506,0.005924162],"study_design_scores_gemma":[0.002580882,0.0002526033,0.00843444,0.0002842545,0.00007025959,0.000052484,0.00005982564,0.9837934,0.0005558972,0.003126159,0.0001206223,0.0006691614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01155622,0.0003492524,0.982269,0.0006419652,0.000914539,0.001070583,0.00004388274,0.000434482,0.002720052],"genre_scores_gemma":[0.2837963,0.0000123144,0.715301,0.0003134506,0.0001532721,0.0001113873,0.0001053583,0.00005140928,0.0001555379],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.27224,"threshold_uncertainty_score":0.9997445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08597512149257881,"score_gpt":0.3449501781571991,"score_spread":0.2589750566646203,"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."}}