{"id":"W4415872580","doi":"10.3390/su17219826","title":"Factors Explaining Municipal Climate Adaptation: Insights from Two Assessments of over 100 German Cities in 2018 and 2022","year":2025,"lang":"en","type":"article","venue":"Sustainability","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Gemeinschaft; Bundesministerium für Bildung und Forschung; European Commission; Trent University; Nottingham Trent University","keywords":"German; Adaptation (eye); Scale (ratio); Climate change; Climate change adaptation; Land use; Climate extremes","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002209049,0.0003626395,0.0003082285,0.001772529,0.0005849004,0.001559991,0.0003648185,0.0003265239,0.0008132352],"category_scores_gemma":[0.004772671,0.0002090447,0.0003975885,0.004803835,0.000697049,0.001196699,0.0021874,0.0004765936,0.0001469781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00330775,"about_ca_system_score_gemma":0.001280713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1199346,"about_ca_topic_score_gemma":0.2190757,"domain_scores_codex":[0.9987675,0.0004683461,0.00009599594,0.0001501909,0.0002953924,0.0002226481],"domain_scores_gemma":[0.9966776,0.0008798887,0.001080281,0.0003115281,0.0008083272,0.0002424465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008360016,0.0000487641,0.9662765,0.00007659272,0.0001218435,0.0002101307,0.01315825,0.003145175,0.0003388396,0.001060987,0.001230826,0.01424841],"study_design_scores_gemma":[0.000002280418,0.0000229991,0.9807257,0.00002413268,0.00002385373,0.0000205087,0.01392404,0.001459707,0.0001306449,0.0002847898,0.003366412,0.00001487034],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975532,0.0001032074,0.000260418,0.0001750673,0.000002092707,0.00001218604,0.0006126794,0.000005090143,0.001276061],"genre_scores_gemma":[0.9989556,0.00007553812,0.0001432336,0.00001336619,0.000001617537,0.0000161125,0.0006007994,0.000002976434,0.0001906697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1199346,"threshold_uncertainty_score":0.2384731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02830400487001018,"score_gpt":0.3288524292457809,"score_spread":0.3005484243757707,"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."}}