{"id":"W3080711308","doi":"10.1002/rob.21983","title":"Efficient obstacle detection based on prior estimation network and spatially constrained mixture model for unmanned surface vehicles","year":2020,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Computer science; Prior probability; Artificial intelligence; Obstacle; Outlier; Maximization; Expectation–maximization algorithm; Set (abstract data type); Pattern recognition (psychology); Mixture model; Computer vision; Mathematics; Mathematical optimization; Maximum likelihood; Statistics; Bayesian probability","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":[0.0001199847,0.00007445452,0.0001287572,0.00001795039,0.00005282226,0.0000242837,0.00004097679,0.00008491072,0.00000571489],"category_scores_gemma":[0.0001423257,0.00006944689,0.00004933972,0.00006065077,0.000008229592,0.00002611728,0.0000042865,0.0001687731,4.171765e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001919666,"about_ca_system_score_gemma":0.00002960975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.515289e-7,"about_ca_topic_score_gemma":0.000002674715,"domain_scores_codex":[0.9994923,0.00001319542,0.000237404,0.00005491229,0.000110854,0.00009137036],"domain_scores_gemma":[0.9994861,0.0002364405,0.00007783459,0.00004161367,0.00007909193,0.00007893429],"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":[0.00009697951,0.00001047623,0.00002974914,0.00007046827,0.00001102725,0.000001663761,0.00009981231,0.9879834,0.00106388,0.00006862405,0.0001891058,0.0103748],"study_design_scores_gemma":[0.0006932804,0.0002014822,0.0000825559,0.00005521598,0.00002557893,0.000003674052,0.00001012622,0.9967568,0.001953674,0.0001231972,0.00002440883,0.00006998663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04150372,0.00005163132,0.9555721,0.002490155,0.0001631005,0.0001227059,0.000003494496,0.00002767494,0.00006545462],"genre_scores_gemma":[0.9134977,0.000009237856,0.0859764,0.000404148,0.0000959715,4.325844e-7,0.000001753347,0.000009168881,0.000005184512],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.871994,"threshold_uncertainty_score":0.2831962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275221812243453,"score_gpt":0.2217484285031756,"score_spread":0.2089962103807411,"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."}}