{"id":"W4385433414","doi":"10.1007/978-3-031-38857-6_11","title":"Wrinkle-Free Sewing with Robotics: The Future of Soft Material Manufacturing","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"European Commission","keywords":"Computer science; Process (computing); Machine vision; Artificial intelligence; Wrinkle; Clothing; Segmentation; Soft robotics; Textile; Robotics; Adaptability; 3D printing; Robot; Computer vision; Mechanical engineering; Engineering; Materials science","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":[0.0005457755,0.0004380969,0.0004423386,0.000541302,0.0002260357,0.001308041,0.0008185172,0.001424927,0.003859279],"category_scores_gemma":[0.0005408628,0.0002502502,0.0004586118,0.0004880471,0.001120475,0.002222979,0.0008637308,0.0007900462,0.001493626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003609412,"about_ca_system_score_gemma":0.0003605763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003048887,"about_ca_topic_score_gemma":0.0002385144,"domain_scores_codex":[0.9997025,0.00005637066,0.00001085224,0.00005766542,0.0001379712,0.00003457799],"domain_scores_gemma":[0.9996157,0.0001430764,0.00004864717,0.00005618581,0.00008832637,0.00004815512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001521786,0.0001642719,0.0007364724,0.001665868,0.0000667092,0.0002078007,0.0001926475,0.023617,0.07128862,0.06011118,0.006612429,0.8351848],"study_design_scores_gemma":[0.00007546588,0.00173816,0.003039759,0.0008855546,0.00008042876,0.001005309,0.0004212623,0.2406351,0.0562018,0.1678079,0.5278266,0.0002827013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.06572644,0.3737182,0.5003457,0.006637317,0.001297722,0.00009150622,0.00009585939,0.001810182,0.05027708],"genre_scores_gemma":[0.5530856,0.158786,0.2610196,0.001283451,0.00123381,0.000128216,0.0002066437,0.0001946377,0.02406212],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.003859279,"threshold_uncertainty_score":0.0129106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041935342342006,"score_gpt":0.2013564272781178,"score_spread":0.1909370738546977,"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."}}