{"id":"W2111952116","doi":"10.1109/icip.2008.4712004","title":"Statistical fusion and sampling of scientific images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Porous medium; Sampling (signal processing); Resolution (logic); Computer science; Scale (ratio); Iterative reconstruction; Image resolution; Fusion; Artificial intelligence; Computer vision; Pattern recognition (psychology); Porosity; Geology; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002024475,0.00003702964,0.00006404889,0.00006605303,0.00008975575,0.00004254006,0.0001689668,0.00001434184,0.00009435734],"category_scores_gemma":[0.0001032909,0.00002968525,0.000008024666,0.0001405406,0.000287913,0.0002152161,0.0001473023,0.00003498248,0.000006594151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004750633,"about_ca_system_score_gemma":0.00002821452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001465239,"about_ca_topic_score_gemma":4.530135e-7,"domain_scores_codex":[0.9993741,0.00002083689,0.0001297805,0.000168455,0.0002242423,0.00008261126],"domain_scores_gemma":[0.9995804,0.0001059601,0.00002861453,0.0001628204,0.00005814296,0.00006399012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003519589,0.0001148139,0.003420098,0.00004870537,0.00000538792,0.00002911001,0.0008886163,8.144389e-7,0.5256907,0.04405067,0.01960258,0.406145],"study_design_scores_gemma":[0.0002296132,0.00008899074,0.02788633,0.00002054764,0.000002329708,0.00005456865,0.00002907891,0.004181725,0.960455,0.006664731,0.0002536696,0.0001333895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01973694,0.00002357101,0.9791309,0.00009704172,0.00005436758,0.00005042194,0.000001704787,0.00009374605,0.0008112919],"genre_scores_gemma":[0.253066,0.00001500085,0.7465933,0.00006938196,0.000004259416,0.000001417945,0.000001437839,0.000001311348,0.0002478946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4347644,"threshold_uncertainty_score":0.1210529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04268697786116465,"score_gpt":0.3174551791289261,"score_spread":0.2747682012677615,"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."}}