{"id":"W2997126203","doi":"10.20380/gi2018.10","title":"MLS2: Sharpness Field Extraction Using CNN for Surface Reconstruction","year":2018,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Kernel (algebra); Pattern recognition (psychology); Point cloud; Field (mathematics); Deep learning; Surface reconstruction; Surface (topology); Algorithm; Mathematics; Geometry","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":[0.0004655005,0.001082277,0.0006596271,0.001067455,0.0002533123,0.0009633991,0.001376263,0.0009475978,0.003152068],"category_scores_gemma":[0.001100256,0.0005361956,0.0007772556,0.0007289644,0.0005522433,0.001470208,0.001153923,0.001017721,0.001307912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008688664,"about_ca_system_score_gemma":0.000789137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005356577,"about_ca_topic_score_gemma":0.007804858,"domain_scores_codex":[0.9997137,0.00002046089,0.00001042295,0.00006495584,0.0001451707,0.00004530904],"domain_scores_gemma":[0.9997199,0.00004977205,0.0000334693,0.00007999199,0.00009396075,0.00002294464],"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":[0.0002083181,0.0001254013,0.003176836,0.0001683482,0.000108653,0.0002532249,0.00008451562,0.3466044,0.0972712,0.006009174,0.005255397,0.5407345],"study_design_scores_gemma":[0.000004593446,0.00003177962,0.00046927,0.000004898319,0.000005778443,0.0000642709,0.000008608574,0.9788963,0.01848894,0.001013484,0.001003806,0.000008329286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0520592,0.0002388719,0.939631,0.0002567898,0.00007099067,0.00008362384,0.0003836148,0.004269858,0.003005943],"genre_scores_gemma":[0.4217333,0.000307115,0.5682437,0.0002279382,0.00005623115,0.00008693268,0.001671795,0.0004709555,0.007202005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005356577,"threshold_uncertainty_score":0.01065081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080234001798355,"score_gpt":0.3476420782358296,"score_spread":0.2396186780559941,"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."}}