{"id":"W6920981728","doi":"10.6084/m9.figshare.27191373.v1","title":"Additional file 3 of COVID-19 and its effects on food producers: panel data evidence from Burkina Faso","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Panel data; Government (linguistics); Food security; Data collection; Work (physics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001729138,0.000778221,0.0007686939,0.003006518,0.0006866987,0.001873603,0.00138039,0.001468666,0.7583132],"category_scores_gemma":[0.02819142,0.0006150435,0.0009399595,0.006293548,0.0002292108,0.001752404,0.001015818,0.001106185,0.1178678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001339008,"about_ca_system_score_gemma":0.002620041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0745248,"about_ca_topic_score_gemma":0.06728627,"domain_scores_codex":[0.9991861,0.0002107402,0.0001027131,0.0001049945,0.0002066844,0.0001888484],"domain_scores_gemma":[0.9742871,0.01897991,0.002394155,0.001008713,0.002582165,0.0007480062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008847204,0.00008031661,0.006387419,0.0005356969,0.00005187361,0.0000556642,0.00006326298,0.001031799,0.00002200566,0.0009960256,0.9865038,0.004183514],"study_design_scores_gemma":[0.004376075,0.0003311209,0.1430021,0.00249462,0.0003464552,0.0003704414,0.002839279,0.007087075,0.0007064718,0.01415782,0.8240823,0.0002062607],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0005076831,0.0000240768,0.0001248748,0.0002193457,0.00002903067,0.00003876673,0.9974791,0.0001153698,0.001461741],"genre_scores_gemma":[0.03436239,0.0002179029,0.002547064,0.0004984505,0.0001419924,0.001173118,0.9369352,0.0004576666,0.02366634],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7583132,"threshold_uncertainty_score":0.3447368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2199052328483143,"score_gpt":0.3060278416419415,"score_spread":0.08612260879362724,"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."}}