{"id":"W2915061717","doi":"10.5539/jas.v11n3p535","title":"Geographical Indication and Regional Development: Cause or Consequence","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Agricultural and Food Sciences","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Federal de Sergipe; Fundação de Apoio à Pesquisa e à Inovação Tecnológica do Estado de Sergipe","keywords":"Geographical indication; Gross domestic product; Per capita; Geography; Certification; Location; Product (mathematics); Agriculture; Agricultural economics; Economic geography; Regional science; Economic growth; Demography; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002518137,0.0003122792,0.0006057338,0.002258229,0.0005337157,0.001617105,0.0008305193,0.0006134853,0.01366151],"category_scores_gemma":[0.007760642,0.0002262704,0.001190314,0.004366338,0.002600303,0.001465152,0.001965246,0.000914023,0.0006498728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009643579,"about_ca_system_score_gemma":0.001734755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022667,"about_ca_topic_score_gemma":0.009138948,"domain_scores_codex":[0.9981187,0.0005147362,0.0001349043,0.0005687971,0.0002874116,0.0003752985],"domain_scores_gemma":[0.9907491,0.00314632,0.002920136,0.0008866992,0.001320186,0.0009775227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001236476,0.00005705403,0.9709626,0.0003368499,0.0003107438,0.000611456,0.001138787,0.000345454,0.0004268349,0.00830825,0.001264544,0.01611368],"study_design_scores_gemma":[0.000008036069,0.000127633,0.9821171,0.000164898,0.000289331,0.0006898181,0.005588886,0.0005066234,0.0003282624,0.00200287,0.008146129,0.00003036028],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9148748,0.02027103,0.00455293,0.01224131,0.0003371304,0.000171683,0.002165284,0.0002317108,0.04515414],"genre_scores_gemma":[0.9969314,0.001485149,0.0002895838,0.0001592432,0.00006187467,0.000009922148,0.000142198,0.00002232296,0.0008982077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01366151,"threshold_uncertainty_score":0.04570228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907387584912893,"score_gpt":0.2316303187440867,"score_spread":0.2025564428949578,"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."}}