{"id":"W2141160399","doi":"10.1109/icra.2011.5980376","title":"Integrating visual exploration and visual search in robotic visual attention: The role of human-robot interaction","year":2011,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Visual search; Set (abstract data type); Artificial intelligence; Gaze-contingency paradigm; Robot; Human visual system model; Visual attention; Human–computer interaction; Visualization; Human–robot interaction; Robot vision; Visual perception; Computer vision; Mobile robot; Perception; Image (mathematics); Psychology","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":[0.0006280694,0.0001432739,0.0001659364,0.0002888304,0.0002037522,0.0001295297,0.0002235372,0.00006969796,0.00006333319],"category_scores_gemma":[0.00002839505,0.0001066812,0.00006402319,0.0005804136,0.00007833741,0.001483526,0.000185388,0.0002399592,0.00002621247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004793063,"about_ca_system_score_gemma":0.00002415639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005402397,"about_ca_topic_score_gemma":0.0004022509,"domain_scores_codex":[0.9984069,0.0002596789,0.0004606515,0.0003469126,0.0003130934,0.0002127607],"domain_scores_gemma":[0.9994793,0.00005284602,0.0001352405,0.0001403951,0.0001388025,0.00005339838],"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":[0.0001144977,0.001709766,0.04035377,0.00007550466,0.00006357556,0.000009022943,0.01966065,0.0003626192,0.4207941,0.1105353,0.00002187641,0.4062994],"study_design_scores_gemma":[0.0005697802,0.001434628,0.08147224,0.00009512468,0.00001249299,0.00002934685,0.01485089,0.826538,0.07085349,0.003849579,0.00001531257,0.0002790957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6436389,0.00002290273,0.3540427,0.0001095274,0.0002211983,0.0002008829,7.97064e-8,0.00007549047,0.001688302],"genre_scores_gemma":[0.9984677,0.000009791896,0.001257092,0.00004308032,0.00004478822,0.00002412502,0.000003355604,0.000008695773,0.0001413922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8261754,"threshold_uncertainty_score":0.4350333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06458631816823952,"score_gpt":0.3450341151448488,"score_spread":0.2804477969766093,"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."}}