{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1328064,0.001678314,0.002987029,0.004315813,0.00149617,0.003162173,0.004502695,0.004291396,0.002042893],"category_scores_gemma":[0.3957244,0.0009862869,0.003143111,0.003115051,0.00518626,0.004339505,0.003731922,0.006205992,0.000412016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001844581,"about_ca_system_score_gemma":0.00218669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001677835,"about_ca_topic_score_gemma":0.0008681353,"domain_scores_codex":[0.9046277,0.067217,0.00566218,0.01082395,0.01051631,0.001152899],"domain_scores_gemma":[0.4366232,0.5106959,0.01643166,0.0271354,0.008038573,0.00107526],"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":[0.003506632,0.0006575384,0.1906918,0.002916378,0.007110392,0.002963584,0.002829318,0.2052363,0.01212319,0.2231066,0.009359692,0.3394987],"study_design_scores_gemma":[0.0006952729,0.00120729,0.03329766,0.0006634429,0.001039421,0.002647438,0.0004749754,0.6835666,0.01986679,0.2456935,0.01046429,0.0003833076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03797824,0.001433619,0.955834,0.0007698248,0.0002792353,0.0005756653,0.0005928664,0.001056934,0.001479662],"genre_scores_gemma":[0.4953511,0.0005573601,0.4977528,0.001025252,0.0002735423,0.002831511,0.001011439,0.0002740062,0.0009228841],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1328064,"threshold_uncertainty_score":0.7023557,"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."}}