{"id":"W2610215883","doi":"10.1115/1.4036639","title":"Tool Accessibility Analysis for Robotic Drilling and Fastening","year":2017,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Aerospace; Rivet; Process (computing); Motion planning; Drilling; Work (physics); Computer science; Path (computing); Software; Engineering; Simulation; Mechanical engineering; Robot; Aerospace engineering; Artificial intelligence","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":[0.0008355897,0.000129573,0.0002677422,0.0003590492,0.0004015249,0.0005982262,0.0002963674,0.00004052818,0.000002181397],"category_scores_gemma":[0.0002223083,0.0001117416,0.0000584934,0.00008680102,0.00007611628,0.000888183,0.00006242395,0.0001332204,9.072198e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005314896,"about_ca_system_score_gemma":0.00001932968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002835799,"about_ca_topic_score_gemma":0.000001142392,"domain_scores_codex":[0.9990864,0.000002095911,0.0002802624,0.0001568533,0.0002326774,0.0002417212],"domain_scores_gemma":[0.9994086,0.00005266798,0.0001464142,0.0001831031,0.0001016229,0.0001075882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003922077,0.000002197339,0.00188056,0.00015172,0.00006030969,0.00000178235,0.0001037564,0.9882825,0.0009983856,0.00001218678,0.000001415724,0.008501257],"study_design_scores_gemma":[0.0002080875,0.00002184409,0.1616693,0.00006395696,0.0001211022,0.00001368424,0.00003075422,0.7984721,0.03910533,0.00007765406,0.0000619713,0.0001541948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7446868,0.000143563,0.2548422,0.00002561874,0.0001963299,0.00004835752,4.6629e-7,0.00002442733,0.0000322533],"genre_scores_gemma":[0.9871573,0.00009958626,0.01262099,0.000005429264,0.00009604321,0.00000201219,1.886792e-7,0.00001169332,0.000006749439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2424705,"threshold_uncertainty_score":0.5768709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427926140971822,"score_gpt":0.2398313365560843,"score_spread":0.225552075146366,"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."}}