{"id":"W4413824988","doi":"10.1115/1.4069652","title":"Visual Telepresence for Underwater Manipulation","year":2025,"lang":"en","type":"article","venue":"Journal of Autonomous Vehicles and Systems","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute","funders":"Natural Science Foundation of Shandong Province; Natural Science Foundation of Jiangsu Province","keywords":"Underwater; Computer science; Human–computer interaction; Computer graphics (images); 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":[0.0001000065,0.00007024178,0.0001677423,0.0001380778,0.0001458902,0.000143493,0.00008195507,0.00004576992,0.000003743115],"category_scores_gemma":[0.0000912336,0.00005372277,0.00006925777,0.00007173459,0.00002622285,0.0002469683,0.00001481367,0.0001016279,0.000002940099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004222527,"about_ca_system_score_gemma":0.00004580399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001966348,"about_ca_topic_score_gemma":0.000002886756,"domain_scores_codex":[0.9992433,0.00005556438,0.0003693143,0.0001129395,0.0001014149,0.0001174968],"domain_scores_gemma":[0.9992838,0.0003153468,0.0002104488,0.000064211,0.000084156,0.0000420568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002068663,0.0001331588,0.002282389,0.0001427709,0.00004365276,0.00002805978,0.0004889708,0.002504848,0.9698282,0.01236553,0.002231386,0.009744138],"study_design_scores_gemma":[0.002479929,0.001055072,0.009059521,0.0005377091,0.0001460617,0.001485005,0.00316415,0.2048543,0.5502495,0.003798165,0.2227334,0.0004372174],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894017,0.00008695371,0.007618467,0.0006501106,0.001052471,0.0001777531,0.000004034584,0.00001378308,0.0009947468],"genre_scores_gemma":[0.9971402,0.00003090975,0.00005279218,0.0001458591,0.0001512979,0.000004086728,2.205988e-7,0.0000055076,0.002469076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4195788,"threshold_uncertainty_score":0.2190751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04698535341013525,"score_gpt":0.3233040970836571,"score_spread":0.2763187436735219,"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."}}