{"id":"W1996184771","doi":"10.1109/iros.2005.1545231","title":"A visually guided swimming robot","year":2005,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; York University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Michigan","keywords":"Mobile robot; Robot; Computer science; Artificial intelligence; Computer vision; Human–computer interaction; Servomotor; Simulation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00003926477,0.0004330356,0.0002437559,0.0001919184,0.0004286105,0.0003261499,0.0004620688,0.0004863031,0.003907042],"category_scores_gemma":[0.0001003133,0.0001315218,0.0002325775,0.0001287175,0.0003610732,0.0003521953,0.0006117562,0.0003443213,0.002506495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001345784,"about_ca_system_score_gemma":0.0003105211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00129753,"about_ca_topic_score_gemma":0.002182696,"domain_scores_codex":[0.9999567,0.000005469642,0.000001544481,0.00001410565,0.00001558458,0.000006510723],"domain_scores_gemma":[0.9999678,0.000004023233,0.000004079546,0.000003983805,0.000007471287,0.0000126553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002198976,0.0001580539,0.002202608,0.0005052668,0.00005536807,0.00175663,0.0003010693,0.01901203,0.4344158,0.0122853,0.01842734,0.5106607],"study_design_scores_gemma":[0.0002343953,0.00305468,0.01015688,0.0002892329,0.000185967,0.008526145,0.0003800217,0.1720201,0.130866,0.0186318,0.6553702,0.0002845707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.180669,0.008908822,0.6451955,0.001466176,0.001327613,0.0004371303,0.0006847491,0.01679255,0.1445185],"genre_scores_gemma":[0.4146892,0.003013287,0.4940538,0.001387958,0.0001801586,0.0003275865,0.001052694,0.0004179043,0.08487734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003907042,"threshold_uncertainty_score":0.0130704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206233165457335,"score_gpt":0.2418442735271997,"score_spread":0.2197819418726264,"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."}}