{"id":"W2564368288","doi":"10.1175/wcas-d-16-0079.1","title":"Impacts of Typhoons on Local Labor Markets based on GMM: An Empirical Study of Guangdong Province, China","year":2016,"lang":"en","type":"article","venue":"Weather Climate and Society","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Typhoon; Quarter (Canadian coin); China; Per capita; Demographic economics; Beijing; Remuneration; Economics; Empirical research; Welfare; Geography; Demography; Meteorology; Statistics; Mathematics; Population; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002255812,0.0001432307,0.0002116675,0.000005152709,0.0001420204,0.00001238514,0.0001472114,0.00009697536,0.00008796893],"category_scores_gemma":[0.00001581864,0.0000355884,0.0001023384,0.0001558159,0.0001377697,0.00007783659,0.0000434154,0.00007611804,0.000004191822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001929644,"about_ca_system_score_gemma":0.000009981413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001688415,"about_ca_topic_score_gemma":0.000207224,"domain_scores_codex":[0.9989677,0.0001008949,0.0001895585,0.0002674267,0.0002375713,0.0002368529],"domain_scores_gemma":[0.9995074,0.0001621432,0.0001032532,0.00007868578,0.00004382208,0.0001047144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003396443,0.002198693,0.8695228,0.00003081575,0.00002817466,0.000004180367,0.00167224,0.00001147431,0.08264813,0.00008248349,0.0007079704,0.0427534],"study_design_scores_gemma":[0.0004171173,0.002503533,0.9910959,0.00006492099,0.00001372654,6.54589e-7,0.00341589,0.0001232141,0.002046712,0.00002405215,0.0001768789,0.0001173881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986147,0.00002413869,0.000001997936,0.0005043715,0.00002472224,0.0002380892,0.00007397361,0.00002401179,0.0004940256],"genre_scores_gemma":[0.9994904,0.0001577344,0.00002071044,0.0001736335,0.00004503739,0.000005477534,0.00000501792,0.000001230772,0.0001007926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215731,"threshold_uncertainty_score":0.1451253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185494047368433,"score_gpt":0.2554303796686403,"score_spread":0.243575439194956,"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."}}