{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004276559,0.0004484389,0.0003103761,0.001178834,0.0002650099,0.0004539507,0.0003450272,0.0003970662,0.001982578],"category_scores_gemma":[0.001853026,0.0002506093,0.000730832,0.0004157721,0.0004572647,0.0005458872,0.000572112,0.0003156228,0.0001514779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082687,"about_ca_system_score_gemma":0.0004589317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001881654,"about_ca_topic_score_gemma":0.00182409,"domain_scores_codex":[0.9996424,0.00006357652,0.00001442147,0.00004811105,0.0001920517,0.00003945546],"domain_scores_gemma":[0.9991002,0.0005579176,0.0001041263,0.00005962656,0.0001557571,0.00002234883],"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":[0.00005921059,0.00003357285,0.002971251,0.0001371485,0.00001778052,0.0002478286,0.0001685859,0.9221339,0.02354823,0.006269899,0.0001355688,0.04427701],"study_design_scores_gemma":[0.000003882748,0.00009228046,0.001753054,0.000009111819,0.00001310325,0.0001266058,0.00006190474,0.9858531,0.008260092,0.002994804,0.0008224224,0.000009712876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.13247,0.0001262871,0.864332,0.00001630773,0.000007398434,0.00006183431,0.00005228796,0.0002311425,0.002702874],"genre_scores_gemma":[0.935023,0.00009584056,0.06393532,0.000003978664,0.000003860669,0.00004558406,0.00007079062,0.00003764871,0.0007840786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001982578,"threshold_uncertainty_score":0.006632328,"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."}}