{"id":"W2342833881","doi":"10.1109/tmi.2015.2511063","title":"Multiple Kernel Point Set Registration","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kernel (algebra); Artificial intelligence; Kernel method; Variable kernel density estimation; Point set registration; Computer science; Gaussian; Kernel embedding of distributions; Set (abstract data type); Point (geometry); Pattern recognition (psychology); Algorithm; Mathematics; Support vector machine; Discrete mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002272784,0.0001221014,0.0001119855,0.00009067076,0.0000630353,0.00004081198,0.00008881611,0.00007176211,0.00009451668],"category_scores_gemma":[0.00004520778,0.0001220101,0.00004992435,0.0001541918,0.00004944329,0.000149173,4.224964e-7,0.0002673376,0.00009163733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009661318,"about_ca_system_score_gemma":0.00004765628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005807301,"about_ca_topic_score_gemma":0.00004959687,"domain_scores_codex":[0.9989461,0.00003095992,0.0002327454,0.0001496724,0.0004461578,0.0001943406],"domain_scores_gemma":[0.9993964,0.00006439041,0.00001827848,0.0001730741,0.0000569204,0.0002909254],"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":[0.00001279847,0.00005451293,0.00007939225,0.00002374759,0.00001880401,0.00003495005,0.000320198,0.9646415,0.000657055,0.00005047371,0.004628404,0.02947823],"study_design_scores_gemma":[0.0005957243,0.00001439276,0.00002376213,0.00004363443,0.00001418631,0.00002513329,0.0001799037,0.9915481,0.005723325,0.0001098671,0.001582353,0.0001395806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005655246,0.00004282147,0.9906018,0.0009368168,0.0009961014,0.00008884557,0.000007297072,0.000362396,0.001308676],"genre_scores_gemma":[0.9980902,0.00003443976,0.001299244,0.0003219724,0.00009757109,0.00001049984,0.00001434102,0.00003108709,0.000100627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.992435,"threshold_uncertainty_score":0.4975426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179565567267006,"score_gpt":0.2486191696932726,"score_spread":0.2268235140206026,"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."}}