{"id":"W3005479798","doi":"","title":"Analysis of error functions for the iterative closest point algorithm","year":2019,"lang":"en","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Error analysis; Computer science; Iterative closest point; Point (geometry); Mathematics; Artificial intelligence; Applied mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01018336,0.001777687,0.001885301,0.002778989,0.0008175128,0.00260061,0.00228998,0.002751915,0.005141443],"category_scores_gemma":[0.04937286,0.0006719083,0.001899594,0.00196718,0.001307249,0.002461955,0.002321986,0.002686019,0.001459975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001721947,"about_ca_system_score_gemma":0.002950484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00814762,"about_ca_topic_score_gemma":0.002539324,"domain_scores_codex":[0.9948565,0.001645576,0.000275462,0.0005708103,0.002250442,0.0004011864],"domain_scores_gemma":[0.9704742,0.02113216,0.001114057,0.001046284,0.005943273,0.0002899464],"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":[0.00024132,0.00005707763,0.002183101,0.0002470251,0.00008455607,0.00009307255,0.0001472934,0.8829077,0.001363026,0.03177781,0.001210372,0.07968763],"study_design_scores_gemma":[0.000006992711,0.00004817743,0.0004703275,0.00003775191,0.000009626999,0.00006434193,0.00002317295,0.9919952,0.000699933,0.005705266,0.0009246908,0.00001449114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0114342,0.00137091,0.9839764,0.0002471461,0.00006363069,0.00005277896,0.00006963941,0.000277986,0.002507314],"genre_scores_gemma":[0.4654103,0.00258404,0.5191427,0.0002230275,0.0001835167,0.0005017564,0.0009705962,0.0007699958,0.01021403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01018336,"threshold_uncertainty_score":0.05385536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261152601280823,"score_gpt":0.2268707105770673,"score_spread":0.2142591845642591,"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."}}