{"id":"W4411048051","doi":"10.1016/j.cities.2025.106101","title":"Smart Citizens Enabling Resilient Neighbourhoods (SCERN): Participatory mapping platform for resilience planning at a neighbourhood scale","year":2025,"lang":"en","type":"article","venue":"Cities","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo; Wilfrid Laurier University","funders":"Social Sciences and Humanities Research Council of Canada; Wilfrid Laurier University","keywords":"Neighbourhood (mathematics); Resilience (materials science); Citizen journalism; Environmental planning; Scale (ratio); Participatory planning; Environmental resource management; Participatory sensing; Business; Process management; Computer science; Geography; Data science; Environmental science; Cartography; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002324803,0.0003241323,0.0003935786,0.0003735168,0.0005068837,0.0001165015,0.0003705594,0.0001578435,0.00008402128],"category_scores_gemma":[0.0001448544,0.000336312,0.0001564754,0.0003489677,0.0002212061,0.0002081213,0.0002242832,0.0002493928,0.00001487533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003110024,"about_ca_system_score_gemma":0.00005271156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002434344,"about_ca_topic_score_gemma":0.00005540235,"domain_scores_codex":[0.9981017,0.00001237547,0.0004642073,0.0003730665,0.0002011132,0.0008475004],"domain_scores_gemma":[0.9989879,0.0003256023,0.00006132089,0.0004748369,0.00006455571,0.00008584691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008398338,0.0002533305,0.2745324,0.008315443,0.001771586,0.0002460142,0.02953577,0.2193934,0.04552076,0.1421317,0.1869614,0.09049826],"study_design_scores_gemma":[0.00551481,0.0005335538,0.07679392,0.004560219,0.0003362116,0.00008479122,0.102201,0.1392686,0.3445087,0.05545578,0.2669008,0.003841651],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9461084,0.004106824,0.01544163,0.0002141238,0.001238759,0.0004612672,0.00005478887,0.001702034,0.03067214],"genre_scores_gemma":[0.9957376,0.0001554076,0.001110959,0.0001809781,0.0001351385,0.0003165243,0.00001628204,0.00005189021,0.002295186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.298988,"threshold_uncertainty_score":0.9999089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.031925121242284,"score_gpt":0.258642919779392,"score_spread":0.226717798537108,"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."}}