{"id":"W2000891157","doi":"10.1108/00021461111177639","title":"Factors affecting variability in farm and off‐farm income","year":2011,"lang":"en","type":"article","venue":"Agricultural Finance Review","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Farm income; Diversification (marketing strategy); Government (linguistics); Business; Payment; Household income; Agriculture; Economics; Agricultural economics; Marketing; Geography; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005306164,0.0003044675,0.0005484467,0.000008192801,0.0001491742,0.00003316037,0.0002720527,0.0001149805,0.0001916944],"category_scores_gemma":[0.0001396733,0.00008683625,0.0001553844,0.0005097092,0.00005345342,0.0001868677,0.0001377095,0.0002213655,0.00003395532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006669646,"about_ca_system_score_gemma":0.000005385264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001201014,"about_ca_topic_score_gemma":0.002490658,"domain_scores_codex":[0.9983937,0.0001588584,0.0004717197,0.0004620726,0.00009054732,0.0004230856],"domain_scores_gemma":[0.999328,0.000224188,0.0002157968,0.00007699655,0.00005195076,0.0001030674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000011844,0.0002186298,0.2127747,0.0007332977,0.00003246914,0.000008719097,0.0008024157,5.675988e-7,0.005639934,0.006247187,0.0003635024,0.7731667],"study_design_scores_gemma":[0.00006399887,0.00007904394,0.985212,0.0006102957,0.00001624476,0.00001371732,0.0001232393,0.000001373385,0.0002456087,0.0002315087,0.01308937,0.0003135642],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795685,0.01518624,1.032796e-7,0.0004540411,0.00009076691,0.0005455673,0.00002342844,0.00004682915,0.004084524],"genre_scores_gemma":[0.9512777,0.04803069,0.0000486405,0.0002796472,0.0001056365,0.00002772273,0.0000422509,0.000001059178,0.0001866334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7728531,"threshold_uncertainty_score":0.354108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04451355716759821,"score_gpt":0.2345518471721699,"score_spread":0.1900382900045717,"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."}}