{"id":"W4322756682","doi":"","title":"Combining Synchrony and shape detection to sustain the robot focus of attention on a selected human partner","year":2013,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroDevNet","funders":"","keywords":"Focus (optics); Robot; Computer science; Human–robot interaction; Artificial intelligence; Human–computer interaction; Computer vision; Physics","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":[0.001026979,0.0001248868,0.0001478198,0.0001008172,0.0002230647,0.0001475763,0.0002086188,0.00006583581,0.00002992215],"category_scores_gemma":[0.0001715237,0.0001065055,0.00003801642,0.00038302,0.00004891684,0.00008701701,0.00006668656,0.000138549,0.00002243696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004771282,"about_ca_system_score_gemma":0.00001119027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005194862,"about_ca_topic_score_gemma":0.0004547295,"domain_scores_codex":[0.9984863,0.0007033118,0.0002736394,0.0001808248,0.0001623976,0.0001934668],"domain_scores_gemma":[0.9985045,0.0002850491,0.00008682211,0.0004539556,0.0005961232,0.00007350581],"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":[0.000004731605,0.0001823125,0.002140126,0.0001526283,0.0001014544,0.000001141717,0.005635879,0.005347163,0.8835113,0.02033769,0.0007724703,0.08181312],"study_design_scores_gemma":[0.0006219496,0.000004339003,0.09958776,0.0009317339,0.00002947872,0.000009117319,0.0004275388,0.6847628,0.2121167,0.0005856978,0.0005787173,0.0003442259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506708,0.000151431,0.04220772,0.001154669,0.00006404232,0.000433177,0.000002340929,0.0002251172,0.005090637],"genre_scores_gemma":[0.9985138,0.00001923057,0.0008099053,0.0000135481,0.000007850012,0.00005194315,0.00001341672,0.00002485508,0.000545436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6794156,"threshold_uncertainty_score":0.434317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008050182693584741,"score_gpt":0.2027762933638933,"score_spread":0.1947261106703086,"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."}}