{"id":"W2347556840","doi":"","title":"Study on Vectorization Method for Engineering Drawing Based on Crosspoint Extracting","year":2000,"lang":"en","type":"article","venue":"Journal of South China University of Technology","topic":"Simulation and Modeling Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Vectorization (mathematics); Constructive; Window (computing); Computer science; Engineering drawing; Computer graphics (images); Algorithm; Programming language; Parallel computing; Engineering; Process (computing); World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001743656,0.00007156353,0.000149201,0.0003986318,0.00007487955,0.000005454261,0.0001306168,0.0000787223,0.00001877248],"category_scores_gemma":[0.00004758966,0.00008142434,0.00006592086,0.0001954498,0.00000996178,0.00004753081,0.000005124689,0.0001953759,0.00000177048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006604749,"about_ca_system_score_gemma":0.00001355088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001893708,"about_ca_topic_score_gemma":4.869773e-7,"domain_scores_codex":[0.9995866,0.000007560603,0.0001579118,0.00007380192,0.00008909772,0.00008503025],"domain_scores_gemma":[0.9996384,0.00006597881,0.0000929911,0.000116203,0.00006149576,0.00002488207],"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":[0.00004915338,0.00006347569,0.0004278334,0.000009241489,0.0000258888,0.000003003433,0.0007571966,0.9861967,0.001719252,0.0001184103,0.000009138263,0.01062068],"study_design_scores_gemma":[0.001536683,0.00039603,0.0050097,0.00007438427,0.0000547463,0.000004995887,0.001542076,0.9873725,0.002237431,0.00007227879,0.001586789,0.0001124294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5427608,0.000004386351,0.4567475,0.00009808834,0.00004509612,0.00009014943,0.000002750993,0.00007528292,0.0001759208],"genre_scores_gemma":[0.9639866,0.000001552551,0.03594561,0.00000477465,0.00002312514,2.446488e-7,8.111073e-7,0.00001119673,0.00002611235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4212257,"threshold_uncertainty_score":0.3320388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117366727302828,"score_gpt":0.2451471016301899,"score_spread":0.2334104288999071,"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."}}