{"id":"W4400797224","doi":"10.1007/s00180-024-01532-y","title":"The root-Gaussian Cox Process for spatial-temporal disease mapping with aggregated data","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian process; Cox process; Process (computing); Computer science; Gaussian; Mathematics; Artificial intelligence; Data mining; Statistics; Poisson process; 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":[0.0004552887,0.0001990476,0.000203306,0.00004802416,0.0003888389,0.0003626669,0.0004053949,0.00003607218,0.00004508592],"category_scores_gemma":[0.002012398,0.0001286023,0.0000247549,0.0002214747,0.0002114733,0.0001058512,0.00008113579,0.0001539506,0.00001328557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003864142,"about_ca_system_score_gemma":0.0004984524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002047809,"about_ca_topic_score_gemma":0.0001015398,"domain_scores_codex":[0.9983647,0.00009165541,0.0003973936,0.0004235677,0.0004358401,0.0002868463],"domain_scores_gemma":[0.9918163,0.007262068,0.0001253049,0.0003463431,0.0002797137,0.0001702874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009085303,0.00004626744,0.0003708488,0.0006380014,0.00008944177,0.00006700937,0.0001394164,0.0001368461,4.644785e-7,0.9159563,0.008043189,0.07442132],"study_design_scores_gemma":[0.0001608809,0.00004251925,0.001651037,0.0001692246,0.00005974831,0.000005666204,0.00003457731,0.4061499,7.515988e-7,0.5892255,0.002368493,0.0001317583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001898673,0.000198692,0.9908843,0.0007396166,0.0002439589,0.00055965,0.006937934,0.0001262099,0.0001197553],"genre_scores_gemma":[0.1383299,0.000004679699,0.8597125,0.00005197833,0.0001695077,0.00007894708,0.001501031,0.00004416062,0.0001072933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.406013,"threshold_uncertainty_score":0.5244249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029831257167444,"score_gpt":0.4018698587570059,"score_spread":0.2988867330402614,"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."}}