{"id":"W4316927665","doi":"10.1007/s11356-023-25365-2","title":"Correction to: Inequality consequences of natural resources, environmental vulnerability, and monetary‑fiscal stability: a global evidence","year":2023,"lang":"en","type":"erratum","venue":"Environmental Science and Pollution Research","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"IBM (Canada); Workers Compensation Board of Alberta","funders":"","keywords":"Vulnerability (computing); Economics; Natural resource; Inequality; Stability (learning theory); Ecotoxicology; Natural resource economics; Natural (archaeology); Environmental science; Development economics; Geography; Ecology; Computer science; Biology; Mathematics","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007098283,0.0003056758,0.0005839157,0.0004543971,0.0007138721,0.0002151552,0.0005945633,0.0002347816,0.0003544153],"category_scores_gemma":[0.001125584,0.00031224,0.00008459952,0.0007569487,0.00415129,0.0004774541,0.001271206,0.0009441317,0.000228753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002024728,"about_ca_system_score_gemma":0.0001415209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003119772,"about_ca_topic_score_gemma":0.0003001149,"domain_scores_codex":[0.996074,0.000163647,0.0008819104,0.001403185,0.0006704301,0.000806824],"domain_scores_gemma":[0.9985783,0.0002184021,0.0003095828,0.0004613944,0.00001588332,0.0004164326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005403217,0.0004477953,0.7146823,0.0004495179,0.000157296,0.0000228155,0.005241125,0.00006834295,0.008486934,0.001380507,0.2138603,0.05466266],"study_design_scores_gemma":[0.0002729267,0.0003911097,0.903851,0.0002087926,0.000008116195,0.00002124447,0.001645807,0.001845618,0.0003759813,0.002627395,0.08820956,0.0005424013],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785814,0.009670251,0.000006573189,0.002020823,0.00558148,0.0007342132,0.001002326,0.00002437385,0.00237856],"genre_scores_gemma":[0.9839877,0.004325299,0.00007992097,0.0001344953,0.0001751282,0.00004165765,0.00005150516,0.00001680874,0.01118752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1891687,"threshold_uncertainty_score":0.9999329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08503397292760662,"score_gpt":0.3133339148547444,"score_spread":0.2282999419271378,"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."}}