{"id":"W4411335618","doi":"10.1162/netn.a.23","title":"Stable brain PET metabolic networks using a multiple sampling scheme","year":2025,"lang":"en","type":"article","venue":"Network Neuroscience","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; Douglas Mental Health University Institute","funders":"Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção; Instituto Serrapilheira; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Fundação de Amparo à Pesquisa do Estado de São Paulo; Alzheimer's Association","keywords":"Scheme (mathematics); Computer science; Sampling scheme; Sampling (signal processing); Artificial intelligence; Neuroscience; Mathematics; Psychology; Statistics; Telecommunications; Detector","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":[0.003140699,0.0005275928,0.0005598288,0.00113758,0.0006070461,0.0007547509,0.0009576724,0.0007121168,0.001341956],"category_scores_gemma":[0.01002507,0.0004007159,0.0008301348,0.0007620372,0.0007670621,0.001046804,0.001020901,0.0008146775,0.0002743142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009555307,"about_ca_system_score_gemma":0.0006796813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004531191,"about_ca_topic_score_gemma":0.005177204,"domain_scores_codex":[0.9990001,0.0005467653,0.00004358617,0.0002120993,0.0001425964,0.00005488715],"domain_scores_gemma":[0.9968045,0.001828613,0.0003717243,0.0004590226,0.0004165615,0.0001196455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004387092,0.0001079776,0.009642392,0.0001018059,0.0001765554,0.0002512203,0.000278625,0.8079058,0.01648557,0.02553638,0.001209363,0.1378656],"study_design_scores_gemma":[0.000006129787,0.00002034086,0.0006358776,0.000003345608,0.000007155787,0.00002319351,0.000007283864,0.9944957,0.0009726739,0.003620676,0.0002023013,0.000005325677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05780338,0.00007816927,0.9412718,0.00009941563,0.0000162667,0.00006483489,0.00008370422,0.0002447821,0.000337647],"genre_scores_gemma":[0.5993769,0.0001163365,0.3984215,0.00006272167,0.00004561306,0.0002287318,0.0004861789,0.0001029251,0.001159142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004531191,"threshold_uncertainty_score":0.01660985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05906868060502402,"score_gpt":0.3618593115236118,"score_spread":0.3027906309185878,"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."}}