{"id":"W4400620956","doi":"10.1016/j.sste.2024.100673","title":"Edge effects in spatial infectious disease models","year":2024,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Population; Context (archaeology); Econometrics; Statistics; Enhanced Data Rates for GSM Evolution; Geography; Computer science; Mathematics; Medicine; Environmental health; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155729,0.0003333562,0.0006609807,0.00007996576,0.0001564564,0.0000384671,0.0001821553,0.000228952,0.0003194213],"category_scores_gemma":[0.0007465488,0.0001517671,0.0001833988,0.0002846746,0.0002062281,0.000246103,0.0001968388,0.0003010823,0.00009287088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004871605,"about_ca_system_score_gemma":0.00002575704,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0224594,"about_ca_topic_score_gemma":0.02209833,"domain_scores_codex":[0.9969129,0.0008534786,0.0006604096,0.0008167792,0.000109674,0.0006467862],"domain_scores_gemma":[0.9975639,0.001851129,0.0001135136,0.00009272081,0.00002987875,0.0003488362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002976969,0.0001052208,0.7557639,0.00021255,0.00003491069,0.0001524902,0.00005854715,0.0004123722,0.0001352397,0.04405218,0.00312783,0.195647],"study_design_scores_gemma":[0.0002310697,0.0003831666,0.6924517,0.00008437505,0.00004182224,0.000003781452,0.00001357737,0.1444091,0.000002419389,0.1501082,0.01192938,0.0003414697],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786438,0.004386286,0.005763548,0.007852723,0.0008112523,0.0006710512,0.00006163043,0.0003035602,0.001506111],"genre_scores_gemma":[0.996213,0.0004120714,0.00008019778,0.001695915,0.0007719969,0.0001231854,0.0004766873,0.0000043809,0.0002225939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1953055,"threshold_uncertainty_score":0.9957458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03683716890609973,"score_gpt":0.2721223771201101,"score_spread":0.2352852082140103,"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."}}