{"id":"W2563210293","doi":"10.1109/ccsse.2016.7784390","title":"Efficient robot vision system for underwater object tracking","year":2016,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Underwater; Robot; Video tracking; Tracking system; Mean-shift; Tracking (education); Hotspot (geology); Machine vision; Mobile robot; Object (grammar); Track (disk drive); Kalman filter; Geography; Pattern recognition (psychology); Geology","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":[0.0002926711,0.000392187,0.0006231685,0.0004342919,0.0004675908,0.0003849088,0.0008894705,0.0007841812,0.002264393],"category_scores_gemma":[0.0003756674,0.0002496678,0.000261019,0.0005160411,0.0001574202,0.0006805696,0.0005826826,0.0006640865,0.001526933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003427802,"about_ca_system_score_gemma":0.000893584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002135568,"about_ca_topic_score_gemma":0.003218668,"domain_scores_codex":[0.9997438,0.00002808162,0.00001008671,0.00006085313,0.0001286959,0.00002844919],"domain_scores_gemma":[0.9998617,0.00001585806,0.00001166854,0.00002274637,0.00007968079,0.00000820878],"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":[0.0002394504,0.0001525194,0.001004025,0.0002541211,0.00006649336,0.0002264081,0.000155258,0.03569682,0.3170013,0.007439014,0.008553014,0.6292117],"study_design_scores_gemma":[0.00007658241,0.0003849753,0.002641419,0.00002549695,0.00007702393,0.0004266235,0.0000401681,0.8834343,0.0836629,0.00296136,0.02620774,0.00006135726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02075115,0.0004727375,0.9722584,0.0001123514,0.0001430714,0.00008284234,0.00007247438,0.002133128,0.003973868],"genre_scores_gemma":[0.4779079,0.0006423312,0.5035467,0.00025883,0.00009535881,0.0002905501,0.0004609068,0.0000877301,0.01670966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002264393,"threshold_uncertainty_score":0.007575095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040419316228909,"score_gpt":0.2368566655829415,"score_spread":0.2164524724206524,"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."}}