{"id":"W4411183390","doi":"10.1038/s41597-025-05143-0","title":"SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre; Hospital for Sick Children; Public Health Ontario; SickKids Foundation; Toronto Metropolitan University; University of Toronto","funders":"Global Affairs Canada","keywords":"Covariate; Settlement (finance); Geography; Statistics; Epidemiology; Econometrics; Computer science; Mathematics; Medicine; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004487057,0.0007834738,0.0007210218,0.003667149,0.0005304703,0.001452318,0.002965071,0.001435074,0.02089026],"category_scores_gemma":[0.03118041,0.0007980209,0.001210342,0.007076542,0.0004273594,0.001731681,0.003053868,0.001359089,0.007113244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009752575,"about_ca_system_score_gemma":0.002248898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01069744,"about_ca_topic_score_gemma":0.01799907,"domain_scores_codex":[0.9976959,0.001048527,0.0003346143,0.0004251258,0.0003952678,0.0001007031],"domain_scores_gemma":[0.9882918,0.004962367,0.001706823,0.003460577,0.001139163,0.0004392958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006550025,0.0002949143,0.05280152,0.003999183,0.0009004574,0.0004273817,0.001347856,0.04455187,0.002068765,0.03593158,0.7603846,0.0966368],"study_design_scores_gemma":[0.0005769966,0.0001556365,0.05908612,0.001122309,0.000239152,0.0004428482,0.0007907044,0.04874381,0.003270645,0.05772505,0.8276411,0.0002055915],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006503656,0.0001564575,0.05336227,0.0004110435,0.00008508167,0.0003030833,0.9326748,0.0039349,0.002568697],"genre_scores_gemma":[0.03073861,0.0002717456,0.07380719,0.0001940509,0.00004364984,0.002768305,0.8898712,0.0007827953,0.001522486],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02089026,"threshold_uncertainty_score":0.0698849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1220016119740402,"score_gpt":0.3688138084758872,"score_spread":0.246812196501847,"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."}}