{"id":"W2337562968","doi":"10.3329/agric.v13i1.26548","title":"Efficiency of Marine Dry Fish Marketing in Bangladesh: A Supply Chain Analysis","year":2016,"lang":"en","type":"article","venue":"The Agriculturists","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Business; Supply chain; Profit (economics); Profit margin; Marketing; Marine fish; Margin (machine learning); Supply and demand; Fishery; Fish <Actinopterygii>; Economics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.00219663,0.0003046313,0.0003227591,0.003905394,0.0006284303,0.002346785,0.0004220373,0.0005099018,0.006167089],"category_scores_gemma":[0.006600847,0.0004396319,0.0005721715,0.006274533,0.0006024222,0.002356521,0.001157855,0.0005179864,0.0005973618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003756274,"about_ca_system_score_gemma":0.00154226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01843435,"about_ca_topic_score_gemma":0.01934592,"domain_scores_codex":[0.9985417,0.0005242887,0.0002014065,0.0001735749,0.0003486821,0.0002103515],"domain_scores_gemma":[0.9913965,0.004849272,0.00169762,0.0002882096,0.001391298,0.000377218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002667378,0.0001935768,0.9554386,0.0002394735,0.0002039454,0.0005462303,0.002137051,0.0111263,0.001028202,0.002623478,0.0003142966,0.02588219],"study_design_scores_gemma":[0.00003208288,0.000752835,0.9121706,0.000222254,0.0002151579,0.0004705939,0.01427705,0.06288759,0.001280637,0.00388689,0.003719806,0.00008448136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934067,0.0002732083,0.001736032,0.0001724282,0.000002442427,0.0001188643,0.0005389398,0.000007382324,0.003743971],"genre_scores_gemma":[0.9978786,0.0002626278,0.0008807165,0.000009664111,0.000002289773,0.00002459706,0.0002976334,0.000003469613,0.0006404386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01843435,"threshold_uncertainty_score":0.03665411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613096600625467,"score_gpt":0.1804480262865737,"score_spread":0.1643170602803191,"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."}}