{"id":"W3121888169","doi":"10.1080/10618600.2021.1873144","title":"False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data","year":2021,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Australian Research Council; Ministry of Science and Technology, Taiwan; Division of Social and Economic Sciences; National Sleep Foundation; National Aeronautics and Space Administration","keywords":"False discovery rate; Nonparametric statistics; Null hypothesis; Statistical hypothesis testing; Inference; SIGNAL (programming language); Null (SQL); Statistical inference; Computer science; Algorithm; Multiple comparisons problem; Pixel; Statistics; Pattern recognition (psychology); Type I and type II errors; Data mining; Mathematics; Artificial intelligence","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":[0.0003042403,0.000122879,0.0002630722,0.00006681012,0.0001141608,0.0002889065,0.0005770335,0.00004871009,0.00002766521],"category_scores_gemma":[0.000834805,0.00010241,0.0000399271,0.0003421028,0.00006338724,0.0003332136,0.0004657241,0.0002044968,0.00000306186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008293552,"about_ca_system_score_gemma":0.0001807937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001916334,"about_ca_topic_score_gemma":0.00001961307,"domain_scores_codex":[0.9985424,0.0001186951,0.000494968,0.0002696488,0.0004191305,0.0001552078],"domain_scores_gemma":[0.9974046,0.001267397,0.0002657144,0.0002189748,0.0006527849,0.0001904941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005414703,0.0008461933,0.02037416,0.0002859778,0.001587097,0.007392416,0.001224084,0.2955158,0.02237264,0.2864963,0.04466203,0.3187018],"study_design_scores_gemma":[0.0004755275,0.0001419542,0.04878321,0.00008126185,0.00002725421,0.0001799908,0.00001418291,0.1081028,0.0006105843,0.836,0.00539613,0.0001871037],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04415839,0.000323678,0.9506686,0.004244935,0.0001525785,0.00003462604,0.0003776029,0.00000946976,0.00003008194],"genre_scores_gemma":[0.6167407,0.00003961537,0.3822317,0.0007258398,0.0001063511,4.714983e-7,0.0001237132,0.000002133788,0.00002944435],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5725823,"threshold_uncertainty_score":0.417616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0286914276213052,"score_gpt":0.2726817872880555,"score_spread":0.2439903596667503,"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."}}