{"id":"W2340634845","doi":"","title":"Weekly Outlook: Corn Consumption and Acreage","year":2016,"lang":"en","type":"article","venue":"farmdoc daily","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bushel; Quarter (Canadian coin); Agricultural economics; Agricultural science; Consumption (sociology); Agribusiness; Economics; Geography; Environmental science; Acre; Agriculture","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000359769,0.001316202,0.0003936375,0.0023982,0.0002523921,0.001258633,0.0004733036,0.000506215,0.04639183],"category_scores_gemma":[0.000934886,0.0002675078,0.0004225254,0.003939851,0.00007945814,0.001118097,0.0004227885,0.001123623,0.04798269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009548682,"about_ca_system_score_gemma":0.0008534925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0468351,"about_ca_topic_score_gemma":0.04336347,"domain_scores_codex":[0.9997173,0.00001721203,0.0000228543,0.00003829397,0.0001639163,0.00004044449],"domain_scores_gemma":[0.9991272,0.00002494932,0.0001160842,0.00002538575,0.0006173744,0.00008907521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001103612,0.00003265138,0.005617603,0.000293898,0.00002782434,0.00003075614,0.00002166653,0.000269065,0.0001882514,0.0007610117,0.9643933,0.02825373],"study_design_scores_gemma":[0.00003201281,0.00006054929,0.08150858,0.0001860328,0.00003078313,0.00008610903,0.0001115275,0.0004993059,0.0002766142,0.0004099121,0.9167739,0.00002469928],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"commentary","genre_scores_codex":[0.008327168,0.004307574,0.0005507887,0.001205601,0.001526256,0.00008117264,0.9062064,0.001059551,0.07673537],"genre_scores_gemma":[0.03176932,0.005204032,0.00139365,0.0006959214,0.0004020561,0.0002177332,0.8630232,0.0002630181,0.09703105],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.0468351,"threshold_uncertainty_score":0.1551961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222679103679502,"score_gpt":0.2135166805568312,"score_spread":0.1912898895200362,"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."}}