{"id":"W1586641054","doi":"10.1109/nssmic.1992.301066","title":"The use of generalized moments for data reduction in maximum likelihood positioning for gamma cameras","year":2003,"lang":"en","type":"article","venue":"IEEE Conference on Nuclear Science Symposium and Medical Imaging","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"","keywords":"Computation; Moment (physics); Scintillation; Truncation (statistics); Computer science; Bijection; Reduction (mathematics); Algorithm; Dimensionality reduction; Method of moments (probability theory); Lookup table; Mathematics; Artificial intelligence; Physics; Combinatorics; Detector; Statistics; Geometry; Machine learning","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":[0.001377237,0.001235048,0.0008401061,0.00131497,0.0006471234,0.00131303,0.001323508,0.0008969683,0.003585248],"category_scores_gemma":[0.01057258,0.0006512009,0.001211796,0.001888455,0.0007036431,0.001278552,0.001713412,0.001689529,0.00204323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007745395,"about_ca_system_score_gemma":0.0009212901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002169967,"about_ca_topic_score_gemma":0.003843475,"domain_scores_codex":[0.998698,0.0006455624,0.00006675425,0.0001131548,0.0004109284,0.0000654518],"domain_scores_gemma":[0.9978999,0.001276487,0.0001820946,0.0003429206,0.0002558861,0.00004269175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002390423,0.00004504822,0.0009644579,0.000339841,0.0001220761,0.0004285664,0.0002580989,0.247512,0.02050447,0.1052487,0.0125349,0.6118028],"study_design_scores_gemma":[0.00003641644,0.00006873201,0.0009810793,0.00004949094,0.0000411919,0.0004357673,0.00006206584,0.8914958,0.0149038,0.07362897,0.0181972,0.00009952525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001202707,0.0001566332,0.9977124,0.00008018769,0.00002822037,0.00001600725,0.00005319036,0.0005172197,0.0002335495],"genre_scores_gemma":[0.02419297,0.0003691594,0.9735768,0.00006592589,0.00008629006,0.0001019444,0.0002747614,0.0003799885,0.0009522321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003585248,"threshold_uncertainty_score":0.01199383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04721296151345156,"score_gpt":0.3041744500509836,"score_spread":0.2569614885375321,"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."}}