{"id":"W3164173259","doi":"10.1177/09544054211019655","title":"Analysis and synthesis of assistive tools for insertion tasks","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Computer science; Task (project management); Snap; Process (computing); Motion (physics); Engineering drawing; Artificial intelligence; Engineering; Programming language; Computer graphics (images); Systems engineering","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":[0.0002156858,0.0007217313,0.0004225743,0.0006598221,0.0003570809,0.00077008,0.0006071728,0.0005425246,0.006446077],"category_scores_gemma":[0.00107241,0.0003187885,0.000394671,0.000406845,0.0003632313,0.0005820234,0.0004343536,0.0002574422,0.001180827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003576533,"about_ca_system_score_gemma":0.0004404666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008269129,"about_ca_topic_score_gemma":0.0007839335,"domain_scores_codex":[0.9997438,0.0000254767,0.00001155677,0.00004699783,0.0001433129,0.00002885988],"domain_scores_gemma":[0.9996843,0.0001383671,0.00003412014,0.00002952773,0.0001040612,0.000009665225],"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":[0.0002722409,0.0001033768,0.0009150089,0.001533591,0.00004637145,0.0006198246,0.0007974009,0.3423505,0.201208,0.05741282,0.00225954,0.3924813],"study_design_scores_gemma":[0.00002625355,0.0003559554,0.00148465,0.00009523494,0.00003822289,0.0002917145,0.0003655886,0.8907369,0.0619788,0.01686007,0.02772819,0.00003842192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04285948,0.0006965457,0.9387608,0.00008251259,0.00005461567,0.0001483122,0.0001966478,0.0007614734,0.01643959],"genre_scores_gemma":[0.6749572,0.0007542429,0.3097672,0.00003566849,0.00002371347,0.0002960623,0.0004715124,0.0001825538,0.01351179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006446077,"threshold_uncertainty_score":0.02156425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419222632035615,"score_gpt":0.2138666880377417,"score_spread":0.1996744617173855,"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."}}