{"id":"W3211857642","doi":"10.23977/cpcs.2021.51005","title":"Research on the Method of Material Scheme Matching Based on Deep Learning","year":2021,"lang":"en","type":"article","venue":"Computing Performance and Communication systems","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Perceptron; Computer science; Plan (archaeology); Scheme (mathematics); sort; Artificial intelligence; Industrial engineering; Data mining; Artificial neural network; Engineering drawing; Engineering; Information retrieval; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00293654,0.00007888208,0.0001351573,0.0001078076,0.0009240505,0.0003387165,0.0005531668,0.00004661538,0.000002432451],"category_scores_gemma":[0.00004969587,0.00005960807,0.00002311217,0.0004102615,0.00007312893,0.0001451858,0.0002643166,0.0004097972,0.000007460982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002305601,"about_ca_system_score_gemma":0.00007218434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001925271,"about_ca_topic_score_gemma":4.843168e-7,"domain_scores_codex":[0.9980861,0.001023484,0.0002507164,0.0001951804,0.0002859324,0.0001586203],"domain_scores_gemma":[0.9981499,0.0007001182,0.0001619912,0.0006830363,0.000281094,0.00002389593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006214494,0.0001623487,0.003844064,0.0007152472,0.00004329823,0.000002470871,0.006463991,0.1588358,0.007147235,0.085684,0.0001678302,0.7368716],"study_design_scores_gemma":[0.0001232724,0.00005365559,0.0007013427,0.0005968161,0.000001404702,0.00002826639,0.001011787,0.990284,0.006567062,0.0001030191,0.0004601166,0.00006927179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6077343,0.0003447193,0.3854089,0.0006440073,0.0003209258,0.0001137822,2.930403e-7,0.00008700824,0.005345987],"genre_scores_gemma":[0.9589537,0.0000466599,0.04081191,0.00005695467,0.00004540063,0.00000686086,0.000003087912,0.000006023387,0.00006937645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8314483,"threshold_uncertainty_score":0.7107143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05506435204944968,"score_gpt":0.3431835104129259,"score_spread":0.2881191583634762,"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."}}