{"id":"W2065938077","doi":"10.7202/021448ar","title":"Des engrais pour du riz : qui gagne, qui perd? Contribution à l’analyse de la dépendance en Malaysia et en Indonésie","year":2005,"lang":"fr","type":"article","venue":"Cahiers de géographie du Québec","topic":"Agriculture, Land Use, Rural Development","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Humanities; Political science; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002120248,0.0006712332,0.0006364507,0.0001326126,0.000832979,0.0003069046,0.0007751622,0.001046125,0.0008632037],"category_scores_gemma":[0.0009953837,0.0003431471,0.0006202561,0.001639341,0.002797851,0.0005343029,0.0001596377,0.001080043,0.0002641883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00150836,"about_ca_system_score_gemma":0.000509103,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01552383,"about_ca_topic_score_gemma":0.07128399,"domain_scores_codex":[0.9950898,0.001688286,0.000663418,0.000768212,0.0004476275,0.001342638],"domain_scores_gemma":[0.9969025,0.001601422,0.0003167048,0.0001748509,0.0003149828,0.0006895388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000261149,0.0008562787,0.7564994,0.00008677372,0.0006312684,0.0002784361,0.009011655,0.0005644115,0.008393196,0.09084147,0.05419624,0.07837977],"study_design_scores_gemma":[0.0008437762,0.0001961828,0.8123284,0.000148824,0.0002576198,0.000400271,0.003123171,0.0001827125,0.001567582,0.006953302,0.1731788,0.0008193187],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622405,0.007348867,0.0002755745,0.02755861,0.0002676819,0.0004399602,0.0001111633,0.0001949905,0.001562658],"genre_scores_gemma":[0.9820226,0.006922896,0.001597593,0.002120071,0.001476498,0.00008836651,0.0001740336,0.00001227445,0.00558562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1189826,"threshold_uncertainty_score":0.999916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004702063923911563,"score_gpt":0.2058236714792227,"score_spread":0.2011216075553111,"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."}}