{"id":"W2991453699","doi":"10.1016/j.agsy.2019.102718","title":"Flows in Agro-food Networks (FAN): An agent-based model to simulate local agricultural material flows","year":2019,"lang":"en","type":"article","venue":"Agricultural Systems","topic":"Agriculture, Land Use, Rural Development","field":"Agricultural and Biological Sciences","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Université de Bordeaux; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche","keywords":"Agriculture; Food waste; Resource (disambiguation); Production (economics); Food processing; Upstream (networking); Circular economy; Business; Environmental science; Environmental economics; Downstream (manufacturing); Food systems; Cropping; Agricultural engineering; Computer science; Food security; Ecology; Economics; Engineering; Marketing; Waste management; Microeconomics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004323283,0.0009596419,0.001030898,0.00005287257,0.0003235699,0.0005612855,0.001057515,0.0006139004,0.0001897798],"category_scores_gemma":[0.00001936741,0.0003089626,0.0003183839,0.001338905,0.00003290444,0.0007417437,0.0002594219,0.0004511627,0.0005624896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900379,"about_ca_system_score_gemma":0.00002635663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112573,"about_ca_topic_score_gemma":0.006485033,"domain_scores_codex":[0.9946224,0.0003549708,0.001190253,0.001362678,0.0009490951,0.001520589],"domain_scores_gemma":[0.9982468,0.0001284742,0.0003101801,0.0002479218,0.0003082282,0.0007583711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002417095,0.0003468798,0.005320471,0.00006199072,0.00007944662,0.00002530636,0.0002852742,0.9029706,0.08403751,0.0005632218,0.003447208,0.002620436],"study_design_scores_gemma":[0.001955652,0.00185809,0.7783769,0.0005165756,0.00007303955,0.00008883046,0.004104584,0.2019993,0.003534559,0.0001097215,0.004176812,0.003205863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936233,0.0001422517,0.00009498871,0.0003034947,0.002130518,0.002662509,0.0001943465,0.0003999015,0.0004486738],"genre_scores_gemma":[0.9952153,0.00001058853,0.0001850431,0.0002370903,0.001164581,0.0002808112,0.001850762,0.0000092233,0.001046621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7730564,"threshold_uncertainty_score":0.9999362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464616980902479,"score_gpt":0.1971194189436216,"score_spread":0.1824732491345968,"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."}}