{"id":"W3013107038","doi":"10.1115/1.4046746","title":"Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing","year":2020,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Vertex (graph theory); Mass customization; Computer science; Artificial intelligence; Measure (data warehouse); Matching (statistics); Fillet (mechanics); Shape analysis (program analysis); Personalization; Computer vision; Pattern recognition (psychology); Mathematics; Theoretical computer science; Data mining; Engineering; Mechanical engineering; Graph","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":[0.0009389105,0.0008282702,0.001401847,0.001301481,0.0004438467,0.0009983535,0.002279335,0.00187343,0.003519919],"category_scores_gemma":[0.002197685,0.0007461118,0.001408154,0.001172233,0.000845859,0.001474273,0.001580352,0.001614516,0.0007862449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213058,"about_ca_system_score_gemma":0.001255653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007789899,"about_ca_topic_score_gemma":0.007429171,"domain_scores_codex":[0.9994718,0.00007344173,0.00002890626,0.0001919153,0.0001494042,0.00008445823],"domain_scores_gemma":[0.9991198,0.0003192265,0.0001327454,0.0001804048,0.0001737034,0.0000740593],"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":[0.000168464,0.0002238285,0.003458705,0.0001173116,0.000104387,0.0001550815,0.00007539324,0.7144301,0.00744326,0.003592146,0.003189644,0.2670417],"study_design_scores_gemma":[0.00000235848,0.00001087584,0.0001298814,0.000002223728,0.000003016669,0.000008208387,0.000003569758,0.9985342,0.0005130395,0.0006883211,0.0001016135,0.000002684768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.148864,0.0006924709,0.8429264,0.0005159102,0.00009441676,0.00007833204,0.0002844005,0.003792289,0.002751804],"genre_scores_gemma":[0.8909231,0.0002971216,0.1021766,0.0003947364,0.00005840765,0.00009211432,0.0008918271,0.0002086861,0.004957254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007789899,"threshold_uncertainty_score":0.01548916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009488341610860554,"score_gpt":0.200079257499731,"score_spread":0.1905909158888704,"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."}}