{"id":"W2613826465","doi":"10.1016/j.engappai.2017.04.015","title":"Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot","year":2017,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Object (grammar); Robot; Segmentation; Context (archaeology); Robotic arm; Automation; Machine vision; Cognitive neuroscience of visual object recognition; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001491429,0.0004636196,0.0004554088,0.0009846376,0.0004085635,0.0005845451,0.0003634199,0.0003824988,0.00287506],"category_scores_gemma":[0.000415289,0.0002977668,0.0004391406,0.0005549486,0.0002517982,0.0006510486,0.000534257,0.0004982693,0.0004646064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002309183,"about_ca_system_score_gemma":0.0005490627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001701822,"about_ca_topic_score_gemma":0.002167345,"domain_scores_codex":[0.9999052,0.00001049976,0.000003841792,0.00001773891,0.00004276323,0.00001986296],"domain_scores_gemma":[0.999837,0.00005873933,0.00001283712,0.00002077579,0.0000586905,0.00001191855],"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":[0.0004825916,0.0001070364,0.001292025,0.0001818356,0.00006246078,0.0002441875,0.0001240091,0.03493937,0.3395907,0.004535589,0.002878102,0.6155621],"study_design_scores_gemma":[0.00002683802,0.0002011081,0.006441226,0.00001791513,0.00005245737,0.0004940141,0.0001187034,0.8786265,0.1052064,0.004489616,0.004284432,0.0000407854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09469125,0.0003680377,0.9001649,0.0001485387,0.00007818082,0.00006358056,0.0001127968,0.00135948,0.003013287],"genre_scores_gemma":[0.6284392,0.0004345797,0.3664349,0.00008044584,0.00004128465,0.00006117843,0.0001970101,0.0001543668,0.004156967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00287506,"threshold_uncertainty_score":0.009618044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05017933423598098,"score_gpt":0.3299027903118674,"score_spread":0.2797234560758864,"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."}}