{"id":"W4411339902","doi":"10.1371/journal.pclm.0000512","title":"Improving an integrative framework of health system resilience and climate change: Lessons from Bangladesh and Haiti","year":2025,"lang":"en","type":"article","venue":"PLOS Climate","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Agence Nationale de la Recherche","keywords":"Resilience (materials science); Climate change; Environmental planning; Environmental resource management; Geography; Political science; Environmental science; Oceanography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005717727,0.0002232013,0.0004785079,0.00008102231,0.0004047452,0.00006569955,0.0001774989,0.0001285586,0.00007226769],"category_scores_gemma":[0.0001051233,0.0001906791,0.00002676561,0.0002659546,0.0002526408,0.000409237,0.0004247136,0.0002379674,0.00001600283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001877337,"about_ca_system_score_gemma":0.00001796532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002641626,"about_ca_topic_score_gemma":0.001277334,"domain_scores_codex":[0.9979674,0.0001727697,0.0004594826,0.0005311926,0.0002155849,0.0006536122],"domain_scores_gemma":[0.9988285,0.0002583893,0.0003006199,0.0003440134,0.00001540963,0.0002530465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003277869,0.0004852671,0.663814,0.005317161,0.000044353,0.00002702376,0.03544223,0.000008870679,0.01492573,0.02070999,0.0003214669,0.2585761],"study_design_scores_gemma":[0.001323381,0.0009322767,0.9291621,0.0114745,0.0001373865,0.00001270204,0.02702962,0.02147433,0.004278264,0.003224586,0.0001973802,0.0007534692],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923983,0.001756298,0.0001823693,0.002796994,0.0001432179,0.0006590034,0.0005219074,0.0001016445,0.001440292],"genre_scores_gemma":[0.9926655,0.003621731,0.002439869,0.0011067,0.00004672556,0.00006301385,0.00002771543,0.00001932335,0.00000946628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2653481,"threshold_uncertainty_score":0.7775669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05243975812027658,"score_gpt":0.3396126091005927,"score_spread":0.2871728509803161,"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."}}