{"id":"W2947202110","doi":"10.1038/s41558-019-0490-0","title":"Tracking global climate change adaptation among governments","year":2019,"lang":"en","type":"article","venue":"Nature Climate Change","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":265,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; PricewaterhouseCoopers (Canada); McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; European Commission; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; McGill University; Institute on the Environment, University of Minnesota; Yale University","keywords":"Adaptation (eye); Vulnerability (computing); Climate change adaptation; Conceptual framework; Tracking (education); Mandate; Process management; Climate change; Political science; Computer science; Environmental resource management; Business; Sociology; Economics; Psychology; Computer security; Social science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005149138,0.000179849,0.0001827985,0.00004108884,0.0003546576,0.0001961715,0.0003983252,0.0003091405,0.0003193883],"category_scores_gemma":[0.00003621202,0.0001673862,0.00008919874,0.0004208177,0.0001121387,0.001268081,0.0001720974,0.0002431616,0.0004650784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245127,"about_ca_system_score_gemma":0.000009281396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002865999,"about_ca_topic_score_gemma":0.002253406,"domain_scores_codex":[0.9977875,0.00009714026,0.0001770617,0.000395188,0.0008113803,0.0007316977],"domain_scores_gemma":[0.9993985,0.00003173338,0.0001743344,0.0002152084,0.00006147199,0.0001186965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006566971,0.000129107,0.7994359,0.0002172207,0.00002338535,0.00002438234,0.02688634,0.000001979452,0.00003604043,0.1118627,0.0005509931,0.06076633],"study_design_scores_gemma":[0.0006695688,0.00008069193,0.9376593,0.0003615577,0.00005128599,6.270037e-7,0.0322541,0.0004076966,0.00001490741,0.0006527594,0.02736036,0.0004872027],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7650429,0.0008290916,0.000003581084,0.001164143,0.002630036,0.001150311,0.0000865788,0.0001810357,0.2289123],"genre_scores_gemma":[0.9957701,0.001771271,0.00005251033,0.001012766,0.0009356844,0.00007235917,0.0000239598,0.00001638016,0.0003449903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2307272,"threshold_uncertainty_score":0.6825812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04397796513209271,"score_gpt":0.3229834843559616,"score_spread":0.2790055192238688,"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."}}