{"id":"W2909695427","doi":"10.1109/iros.2018.8594410","title":"Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection","year":2018,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Underwater; Artificial intelligence; Coral reef; Obstacle avoidance; Monocular; Computer vision; Controller (irrigation); Collision avoidance; Convolutional neural network; Robot; Remotely operated underwater vehicle; Path (computing); Mobile robot; Collision; Simulation; Real-time computing; Geology; Oceanography","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":[0.0001901621,0.0001087988,0.0001398168,0.00009092118,0.0001095144,0.00006948783,0.00008842008,0.00007194798,0.00001835424],"category_scores_gemma":[0.000002420631,0.00009884598,0.00002329517,0.0001310788,0.00003105884,0.0001139777,0.00002110381,0.00006735303,0.00001540262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008841324,"about_ca_system_score_gemma":0.00001628488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006426874,"about_ca_topic_score_gemma":0.0003785334,"domain_scores_codex":[0.9993427,0.00002957787,0.0002331841,0.0001440133,0.00006662004,0.0001839266],"domain_scores_gemma":[0.9996558,0.00006709355,0.00002509927,0.0001443589,0.00006399125,0.00004361552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001337288,0.0007459237,0.08084276,0.001061725,0.000190653,0.000006793149,0.007565858,0.1158104,0.7524803,0.001926269,0.004876093,0.033156],"study_design_scores_gemma":[0.001152414,0.0002233867,0.002071283,0.00004761489,0.00000358411,0.000002473892,0.0001097542,0.8518659,0.1319302,0.0004359794,0.01198677,0.0001706153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6859852,0.00005976811,0.3129346,0.0001753662,0.00007158263,0.0003120614,0.000002607036,0.0001826502,0.0002761265],"genre_scores_gemma":[0.9796236,0.00000489489,0.01993421,0.0000925289,0.00003611005,0.00004900222,0.00001120838,0.00002378531,0.0002246317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7360556,"threshold_uncertainty_score":0.4030822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152120658202899,"score_gpt":0.2375646513390525,"score_spread":0.2260434447570235,"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."}}