{"id":"W3139148610","doi":"10.1016/j.gfs.2021.100526","title":"The impact of the COVID-19 pandemic on fish consumption and household food security in Dhaka city, Bangladesh","year":2021,"lang":"en","type":"article","venue":"Global Food Security","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":107,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Consumption (sociology); Pandemic; Food security; Livelihood; Socioeconomics; Business; Dried fish; Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Agricultural economics; Household income; Geography; Environmental health; Fish <Actinopterygii>; Agriculture; Fishery; Economics; Infectious disease (medical specialty); Biology; Medicine; Disease","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006636651,0.000340537,0.0003626297,0.0008282717,0.0005462801,0.001736269,0.0003908098,0.0012482,0.00669215],"category_scores_gemma":[0.002392826,0.0002905384,0.0005428536,0.00220408,0.0008969756,0.001490613,0.00161543,0.001708146,0.0005532148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005799138,"about_ca_system_score_gemma":0.002418432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3296193,"about_ca_topic_score_gemma":0.3264685,"domain_scores_codex":[0.9994625,0.0001420338,0.00002725149,0.0000401683,0.00004099571,0.0002869679],"domain_scores_gemma":[0.9987546,0.0003803571,0.0003350004,0.00003547603,0.0002129614,0.0002814996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005525345,0.0002392416,0.9150658,0.0002158842,0.0003807022,0.002834575,0.001336711,0.02544843,0.0005426799,0.025907,0.01692558,0.01055103],"study_design_scores_gemma":[0.00005815189,0.000217973,0.9285613,0.0001592887,0.0002069961,0.0003313826,0.02097673,0.0300546,0.0003078812,0.008861165,0.01017268,0.00009183472],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661313,0.001444854,0.000294515,0.01499764,0.00006423613,0.00003438496,0.006933269,0.00001270447,0.01008702],"genre_scores_gemma":[0.9960019,0.0008701423,0.0000403417,0.0002009664,0.00001702379,0.00001208089,0.0009365515,0.000002568875,0.001918309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3296193,"threshold_uncertainty_score":0.6554013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09477912715005972,"score_gpt":0.3045824056131929,"score_spread":0.2098032784631332,"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."}}