{"id":"W3083449333","doi":"10.26083/tuprints-00019337","title":"Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography","year":2021,"lang":"en","type":"article","venue":"TUbilio (Technical University of Darmstadt)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image resolution; Tomography; Context (archaeology); Measure (data warehouse); Computer science; Covariance; Spatial analysis; Tomographic reconstruction; Rank (graph theory); Resolution (logic); Iterative reconstruction; Artificial intelligence; Mathematics; Statistics; Data mining; Optics; Physics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637533,0.0001011635,0.0002756932,0.00009367624,0.0001165706,0.00000546184,0.0003222986,0.0001200467,0.0001018885],"category_scores_gemma":[0.0002631537,0.00007269696,0.000218466,0.0007550418,0.0003809657,0.00003828332,0.0002813951,0.0004224333,0.000001852237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004231099,"about_ca_system_score_gemma":0.0001380015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007628802,"about_ca_topic_score_gemma":0.0003669265,"domain_scores_codex":[0.998902,0.00008928332,0.0002724898,0.0002463614,0.000305369,0.0001845152],"domain_scores_gemma":[0.9988301,0.000166934,0.0001280399,0.0006727851,0.0001316898,0.00007039966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002285091,0.004471421,0.1687625,0.001633085,0.0002200512,0.0004117097,0.001895102,0.000206357,0.7402077,0.01151612,0.02065969,0.04773123],"study_design_scores_gemma":[0.013656,0.002024084,0.5503636,0.006409718,0.001661484,0.0006044097,0.00441535,0.05431443,0.282827,0.006620161,0.07577348,0.001330317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8202559,0.0001868891,0.133577,0.04183493,0.0001293799,0.00105128,0.00004876907,0.000169198,0.002746594],"genre_scores_gemma":[0.9820297,0.0000861263,0.01751312,0.0002526032,0.00001430914,0.000001323868,0.00001092376,0.00000754345,0.0000843149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4573807,"threshold_uncertainty_score":0.2964496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02695175943446082,"score_gpt":0.2908906769791284,"score_spread":0.2639389175446676,"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."}}