{"id":"W4220846098","doi":"10.1007/s44177-022-00017-1","title":"Analyzing Socio-Metabolic Vulnerability: Evidence from the Comoros Archipelago","year":2022,"lang":"en","type":"article","venue":"Anthropocene Science","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vulnerability (computing); Archipelago; Resource (disambiguation); Leverage (statistics); Small Island Developing States; Economic geography; Geography; Psychological resilience; Natural resource economics; Emerging markets; Development economics; Climate change; Environmental resource management; Business; Economics; Ecology","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":["sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.005591921,0.0001322544,0.0001703516,0.0001072149,0.010563,0.0003511846,0.001467734,0.00002803815,0.001589821],"category_scores_gemma":[0.0021321,0.0001114591,0.00008267713,0.002489015,0.004855033,0.001047609,0.0003973116,0.0002996004,0.00004732368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000415417,"about_ca_system_score_gemma":0.001066795,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03554764,"about_ca_topic_score_gemma":0.01224135,"domain_scores_codex":[0.9961596,0.0007841618,0.0002861049,0.000601385,0.001593998,0.0005748105],"domain_scores_gemma":[0.9977703,0.00113656,0.0001953705,0.0005255942,0.000232192,0.0001399468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00003894022,0.0002525584,0.4040011,0.000007858877,0.00002077461,0.000008542247,0.4355182,0.001152093,0.03087107,0.03826296,0.002246849,0.08761911],"study_design_scores_gemma":[0.0004210503,0.0001545651,0.5284733,0.00008340332,0.00009509389,0.000004659254,0.4100765,0.007778384,0.004727058,0.02843176,0.01895011,0.0008041156],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791531,0.00219757,0.001393297,0.01498704,0.0009928256,0.0003840539,0.00004644024,0.0001068499,0.0007388052],"genre_scores_gemma":[0.9970493,0.00109633,0.0007656883,0.0006008139,0.0003176141,0.00005551801,0.000008685942,0.000009988155,0.00009602649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1244722,"threshold_uncertainty_score":0.9993228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1458781778445389,"score_gpt":0.3824835392870632,"score_spread":0.2366053614425244,"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."}}