{"id":"W3160560371","doi":"10.5751/es-12280-260220","title":"Assessment of urban resilience based on the transformation of resource-based cities: a case study of Panzhihua, China","year":2021,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; International Centre for Integrated Mountain Development","keywords":"China; Resilience (materials science); Resource (disambiguation); Transformation (genetics); Geography; Urban resilience; Fang; Zhàng; Psychological resilience; Environmental resource management; Environmental planning; Ecology; Political science; Urban planning; Environmental science; Archaeology; Biology; Computer science; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001116211,0.0006169639,0.000310472,0.003074232,0.001305756,0.001026397,0.001216737,0.0006412807,0.0009814019],"category_scores_gemma":[0.001556801,0.0002595326,0.0007066466,0.003943005,0.001661495,0.001090676,0.002005641,0.00038364,0.00005406953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007604128,"about_ca_system_score_gemma":0.003115992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2035928,"about_ca_topic_score_gemma":0.3285941,"domain_scores_codex":[0.9993655,0.0002282462,0.00002855109,0.00006465625,0.0001418056,0.0001712239],"domain_scores_gemma":[0.9992886,0.0001939073,0.0001328055,0.00006989703,0.0001664362,0.0001483082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000393849,0.0005860916,0.7949496,0.000375006,0.0005722124,0.01981934,0.01560289,0.07189737,0.002987844,0.01482797,0.002794178,0.07519365],"study_design_scores_gemma":[0.0000517138,0.0003912562,0.8031659,0.000164469,0.0003755939,0.001447669,0.05805734,0.1230074,0.002249987,0.004859538,0.006096147,0.0001328738],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964494,0.0001477976,0.000990579,0.0002373842,0.000004676589,0.00006819284,0.0001927253,0.00001330827,0.001895971],"genre_scores_gemma":[0.9989949,0.00007211453,0.0005925331,0.000008105654,0.000001103061,0.00001514489,0.00006820788,0.000001813651,0.0002461602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2035928,"threshold_uncertainty_score":0.4048156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01097996643063516,"score_gpt":0.264531429514531,"score_spread":0.2535514630838958,"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."}}