{"id":"W1985558635","doi":"10.1109/iros.2010.5652751","title":"Swimming with robots: Human robot communication at depth","year":2010,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Task (project management); Computer science; Human–computer interaction; Underwater; Domain (mathematical analysis); Task analysis; Telerobotics; Communications system; Mobile robot; Simulation; Artificial intelligence; Engineering; Systems engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001110937,0.0001047743,0.0001052954,0.00003971487,0.0002027311,0.00006189046,0.000374861,0.00006097386,0.000126644],"category_scores_gemma":[4.811048e-7,0.00008467457,0.00002411116,0.00008616639,0.00003609375,0.0001330237,0.00009042266,0.0002121596,0.0001109748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000341398,"about_ca_system_score_gemma":0.000003792358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001253126,"about_ca_topic_score_gemma":0.00564915,"domain_scores_codex":[0.9994701,0.00002139835,0.0001752886,0.00008866656,0.00009931707,0.0001452337],"domain_scores_gemma":[0.9990351,0.00002423497,0.00002844094,0.0008217208,0.00003529389,0.00005516834],"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":[0.000007688598,0.00006426071,0.01984833,0.00005666494,0.0001003023,0.000001632377,0.001507511,0.01049899,0.9513829,0.005189486,0.0007388077,0.01060339],"study_design_scores_gemma":[0.002993432,0.0001762916,0.07060031,0.0002150895,0.00008605405,0.0002946501,0.001733739,0.04992588,0.5792343,0.0009784936,0.2916447,0.002117092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8556394,0.0001831816,0.04075686,0.0002333323,0.00005274097,0.0002275231,0.000001141726,0.0008825648,0.1020232],"genre_scores_gemma":[0.982623,0.0000177145,0.01559321,0.00003596456,0.00002374066,0.00002900817,0.00002115803,0.00003156629,0.001624631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3721486,"threshold_uncertainty_score":0.3452929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118786843721387,"score_gpt":0.2146991697404685,"score_spread":0.2028204853683298,"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."}}