{"id":"W2136716792","doi":"10.1109/tim.2006.870114","title":"Neural-Network-Based Adaptive Sampling of Three-Dimensional-Object Surface Elastic Properties","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial neural network; Self-organizing map; Adaptive sampling; Curse of dimensionality; Neural gas; Sampling (signal processing); Computer science; Dimensionality reduction; Artificial intelligence; Surface (topology); Set (abstract data type); Pattern recognition (psychology); Computer vision; Time delay neural network; 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":[0.0005370672,0.0002581336,0.0003049678,0.0003758479,0.0001400907,0.0003439169,0.0004950003,0.0003239145,0.0004492152],"category_scores_gemma":[0.002005313,0.0001458452,0.0002560172,0.0005104471,0.0003015015,0.0005985199,0.0003936343,0.0002996735,0.0001213986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003679047,"about_ca_system_score_gemma":0.0002351968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001990912,"about_ca_topic_score_gemma":0.002838378,"domain_scores_codex":[0.9998285,0.00004378219,0.000007172291,0.00002578549,0.0000775447,0.00001718005],"domain_scores_gemma":[0.9995707,0.0002437455,0.00003316584,0.00004726763,0.00009325199,0.00001191033],"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":[0.0002508597,0.00008302171,0.002583063,0.0001338405,0.00005732134,0.00011321,0.000121249,0.5379848,0.06952285,0.007513967,0.0007979164,0.3808379],"study_design_scores_gemma":[0.000001987918,0.00001127892,0.0004967595,0.00000216203,0.000003028573,0.00001965198,0.000005079078,0.992469,0.00596243,0.0008130951,0.0002112509,0.000004193299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06229683,0.0001890295,0.9358214,0.00006095977,0.00002270665,0.0000222224,0.00002831145,0.0002881715,0.001270483],"genre_scores_gemma":[0.7717158,0.0002173727,0.2268454,0.00003751885,0.00001782418,0.00005502463,0.00009446623,0.00004267268,0.000973981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001990912,"threshold_uncertainty_score":0.003958642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05131570623221309,"score_gpt":0.2323570807027529,"score_spread":0.1810413744705398,"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."}}