{"id":"W7077144144","doi":"10.5285/cdfea06f-d47c-4967-99d4-cc71bddea45d","title":"Exposure datasets representing a synthetic future urban context, including socio-demographic, building, and land-use data from 10 cities, for natural hazard risk modelling","year":2025,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"Natural Environment Research Council","keywords":"Identifier; Footprint; Context (archaeology); Population; Land use; Hazard; Point (geometry); Plan (archaeology); Data type","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006588,0.0009186745,0.000539402,0.001356533,0.0003700675,0.0007631741,0.001725919,0.001693796,0.006363664],"category_scores_gemma":[0.002464727,0.0002949172,0.001227048,0.002478083,0.0003448167,0.0005858109,0.001119351,0.0009486507,0.003458235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135389,"about_ca_system_score_gemma":0.000867558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01318072,"about_ca_topic_score_gemma":0.02211009,"domain_scores_codex":[0.9993173,0.0001995139,0.00006771669,0.0001611299,0.0001698724,0.00008455775],"domain_scores_gemma":[0.9987586,0.0004169204,0.0001074654,0.0002571076,0.0003515769,0.0001083096],"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":[0.001047333,0.00190886,0.08561924,0.003511905,0.0006298035,0.001641762,0.0005870688,0.2298399,0.003639197,0.00674558,0.5943793,0.0704501],"study_design_scores_gemma":[0.0007293247,0.0009168379,0.1920692,0.0007487268,0.0003180824,0.00153137,0.002335871,0.223096,0.007674709,0.008875441,0.5613921,0.0003123716],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08068047,0.0005266687,0.008219995,0.000885717,0.000246131,0.0003699161,0.9034548,0.00143642,0.004179841],"genre_scores_gemma":[0.1074445,0.0002972712,0.01147576,0.0002449707,0.00004826019,0.0006823611,0.8774469,0.00008258153,0.002277488],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01318072,"threshold_uncertainty_score":0.02620804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06179118194902603,"score_gpt":0.3057150809426517,"score_spread":0.2439238989936256,"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."}}