{"id":"W4396671011","doi":"10.1016/j.rcim.2024.102773","title":"Corrigendum to ’In-situ Elastic Calibration of Robots: Minimally-Invasive Technology, Cover-Based Pose Search and Aerospace Case Studies’, Robotics and Computer-Integrated Manufacturing 89 (2024), 102743.","year":2024,"lang":"en","type":"erratum","venue":"Robotics and Computer-Integrated Manufacturing","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; National Research Council Canada","funders":"","keywords":"Aerospace; Cover (algebra); Robotics; Calibration; Robot; Artificial intelligence; Computer science; Engineering; Aerospace engineering; Mechanical engineering; Mathematics","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":[0.002858334,0.002627295,0.002474559,0.004873648,0.004611773,0.003924294,0.003803489,0.008281923,0.1160775],"category_scores_gemma":[0.02595947,0.001296723,0.001989595,0.003427367,0.002360576,0.003285331,0.002568263,0.007786281,0.08073943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006746143,"about_ca_system_score_gemma":0.003440464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06523669,"about_ca_topic_score_gemma":0.1204218,"domain_scores_codex":[0.9942843,0.0006276977,0.0005380542,0.0007617703,0.003402975,0.0003852436],"domain_scores_gemma":[0.9850085,0.002801839,0.0004241537,0.0009342851,0.01031414,0.0005171653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001061857,0.000005940426,0.00001806191,0.00006291409,0.000004126732,0.00005819146,0.00001053193,0.00004979767,0.00005713302,0.0008757769,0.9953246,0.003522209],"study_design_scores_gemma":[0.00001104976,0.00001828161,0.0005236949,0.0001452151,0.00001515676,0.0001419104,0.00005295561,0.0002632944,0.0002634285,0.002113939,0.9964221,0.00002909106],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001312071,0.004318419,0.001818898,0.04098034,0.9148911,0.00006177875,0.001290143,0.0004459675,0.03606217],"genre_scores_gemma":[0.004831228,0.01157254,0.004100901,0.03862361,0.1207608,0.0002413006,0.002821539,0.001201092,0.815847],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1160775,"threshold_uncertainty_score":0.3883179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507581137934844,"score_gpt":0.2315305293098983,"score_spread":0.2164547179305499,"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."}}