{"id":"W2806990741","doi":"10.1093/ajae/aay023","title":"A Century of U.S. Farm Productivity Growth: A Surge Then a Slowdown","year":2018,"lang":"en","type":"article","venue":"American Journal of Agricultural Economics","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of California, Davis; Cooperative State Research, Education, and Extension Service; Giannini Foundation of Agricultural Economics; Agricultural Research Service; University of Wyoming; U.S. Department of Agriculture","keywords":"Productivity; Slowdown; Agricultural economics; Economics; Quarter (Canadian coin); Total factor productivity; Agricultural productivity; Agriculture; Population growth; Multifactor productivity; Population; Geography; Demography; Macroeconomics; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008762176,0.000317648,0.001247408,0.0002683171,0.00009940633,0.00007613866,0.000582564,0.00009445435,0.0001065087],"category_scores_gemma":[0.0001702217,0.0002459021,0.000521566,0.0003793625,0.0004770454,0.0006319841,0.0001176957,0.0002950806,0.0001084881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001982322,"about_ca_system_score_gemma":0.00006202822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002784414,"about_ca_topic_score_gemma":0.0001156204,"domain_scores_codex":[0.9974118,0.00004131833,0.001642074,0.0004263233,0.00004539204,0.0004331038],"domain_scores_gemma":[0.9955148,0.0001127224,0.003445864,0.0003077276,0.0003965825,0.0002223773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.002623353,0.003565064,0.3377708,0.0002944101,0.006135836,0.00003147759,0.02085307,0.0003296164,0.01056797,0.5111395,0.02488913,0.08179971],"study_design_scores_gemma":[0.002587557,0.005328598,0.8603542,0.0001233627,0.0001495029,0.0007221687,0.004210409,0.00003412619,0.006179621,0.0158541,0.1026696,0.001786762],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834239,0.0008346926,0.00004268114,0.001475257,0.0009389489,0.0001763567,0.0001424774,0.00001583685,0.01294981],"genre_scores_gemma":[0.9953716,0.001449179,0.001702982,0.0001972558,0.001096214,0.00000434893,0.00001094837,0.00002327954,0.0001441988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5225834,"threshold_uncertainty_score":0.9999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009300838694216407,"score_gpt":0.1789590224227156,"score_spread":0.1696581837284991,"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."}}