{"id":"W4400164612","doi":"10.31435/rsglobal_ijitss/30062024/8155","title":"IDENTIFYING COVIDOGENIC ENVIRONMENTS IN URBAN SECTORS OF KHROUB CITY (ALGERIA): A GIS-BASED APPROACH TO ASSESSING PANDEMIC RISK AND VULNERABILITY","year":2024,"lang":"en","type":"article","venue":"International Journal of Innovative Technologies in Social Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Vulnerability (computing); Pandemic; Environmental planning; Vulnerability assessment; Environmental resource management; Geography; Scale (ratio); Environmental health; Public health; Coronavirus disease 2019 (COVID-19); Geographic information system; Business; Cartography; Computer science; Medicine; Computer security; Environmental science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006097578,0.0001437307,0.0003997781,0.0008822235,0.0001200038,0.00008071051,0.0008663865,0.0001182034,0.000003456353],"category_scores_gemma":[0.02045402,0.0001123016,0.00006115607,0.002589176,0.001691275,0.0004129147,0.000577128,0.0006610281,2.664314e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359555,"about_ca_system_score_gemma":0.00020338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006315587,"about_ca_topic_score_gemma":0.00001488087,"domain_scores_codex":[0.9976529,0.0001529041,0.0008953917,0.0003430574,0.0007160071,0.0002397476],"domain_scores_gemma":[0.9974237,0.001639775,0.0005500423,0.0001040103,0.0002610834,0.00002136145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005177413,0.000217482,0.9319328,0.00005360319,0.00005619046,0.00001783062,0.002412484,0.0001180017,0.02326314,0.02353099,0.00006263269,0.01828305],"study_design_scores_gemma":[0.0006880764,0.0001424738,0.6202158,0.0004799201,0.00001395297,0.00001148973,0.005960013,0.00145758,0.009692373,0.3608853,0.0001635787,0.0002894445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330242,0.0001360706,0.06560779,0.0005716329,0.0002161227,0.0001641164,0.00001193183,0.00005835695,0.0002098183],"genre_scores_gemma":[0.9864715,0.00004261777,0.01338624,0.00004524831,0.00003286102,0.00001373778,3.036793e-7,0.000005508847,0.000001964321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3373543,"threshold_uncertainty_score":0.9877971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2265923572975839,"score_gpt":0.4754071981522247,"score_spread":0.2488148408546408,"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."}}