{"id":"W3180986011","doi":"10.3390/ijgi10070461","title":"Assessing Earthquake Impacts and Monitoring Resilience of Historic Areas: Methods for GIS Tools","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Circulatory and Respiratory Health; Horizon 2020; Università degli Studi di Camerino","keywords":"Damages; Resilience (materials science); Vulnerability (computing); Hazard; Natural hazard; Computer science; Vulnerability assessment; Environmental resource management; Geographic information system; Environmental science; Geography; Computer security; Remote sensing; Psychological resilience","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":[],"consensus_categories":[],"category_scores_codex":[0.0006484934,0.0000920603,0.0002036454,0.0002757302,0.00005364258,0.0002222258,0.0001667179,0.00006328188,0.00001994791],"category_scores_gemma":[0.001004591,0.00008633803,0.000118739,0.00016382,0.00002850536,0.004524669,0.00002334117,0.0001499287,8.521742e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873906,"about_ca_system_score_gemma":0.0000976634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007371023,"about_ca_topic_score_gemma":0.000001514356,"domain_scores_codex":[0.9988232,0.00003858243,0.0006470826,0.00004885502,0.0003248114,0.0001174736],"domain_scores_gemma":[0.9982162,0.0002481305,0.0003281406,0.00008891799,0.001058478,0.00006014662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004819681,0.00001440595,0.004539408,0.0001901871,0.0002387071,0.000003733908,0.001407929,0.1111887,0.01721571,0.0003617264,0.0001715897,0.8646197],"study_design_scores_gemma":[0.002812755,0.0002743183,0.3452409,0.001434366,0.000380721,0.0008112811,0.007906411,0.1666605,0.4278767,0.004509422,0.04129945,0.0007931846],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3635274,0.000533856,0.634231,0.0001177984,0.001159225,0.0000469567,0.00001147185,0.00001146613,0.0003608095],"genre_scores_gemma":[0.9272615,0.0002957995,0.07223537,0.00002380565,0.0001619926,0.000001874041,0.00001014905,0.000004168933,0.000005373298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8638266,"threshold_uncertainty_score":0.3520763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546009219673659,"score_gpt":0.3384879528427809,"score_spread":0.3230278606460443,"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."}}