{"id":"W6931371639","doi":"10.5281/zenodo.7400798","title":"Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Nowcasting; Unemployment; Unemployment rate; Leverage (statistics); Youth unemployment; Economic indicator; Proxy (statistics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006890121,0.0004592982,0.0002952615,0.0009249864,0.0003696488,0.0005636342,0.0003066612,0.0008277545,0.0006087216],"category_scores_gemma":[0.002787855,0.0001245215,0.0003858682,0.0009015743,0.0002246202,0.0008060368,0.0005260344,0.0005834025,0.0002488721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005393647,"about_ca_system_score_gemma":0.0003612867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03758282,"about_ca_topic_score_gemma":0.0381804,"domain_scores_codex":[0.9997446,0.00009246001,0.0000229121,0.00005047264,0.00003735505,0.00005231015],"domain_scores_gemma":[0.999342,0.0003140405,0.0001174563,0.00005321158,0.0001131867,0.00006007174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001698627,0.0004584345,0.6990724,0.0008896254,0.0004211569,0.005227971,0.004260569,0.1233612,0.01492466,0.003404845,0.02789315,0.1183876],"study_design_scores_gemma":[0.00006984799,0.0001959662,0.4765401,0.0002340759,0.0001631955,0.0004385634,0.008580639,0.4888992,0.00532806,0.001302567,0.01813286,0.0001148668],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904398,0.0003994686,0.001553372,0.001625777,0.0001364504,0.00003296899,0.004302805,0.00009334465,0.001415951],"genre_scores_gemma":[0.9925998,0.0002632359,0.002007403,0.00007749136,0.0000866562,0.00002646096,0.004252943,0.00001593246,0.0006701009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03758282,"threshold_uncertainty_score":0.07472813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139302221826702,"score_gpt":0.2990858868257691,"score_spread":0.1597836649990671,"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."}}