{"id":"W6920376112","doi":"10.6068/dp158cfda1a9361","title":"Trend 1960 - 2015. United States Department of Agriculture. World Agricultural Production, Supply, and Distribution: Agricultural Commodities | Country: Canada | Commodity: Cotton | Attribute: USE Dom. Consumption, 1960-2015. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 008-007-001.","year":2016,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Commodity; Agricultural productivity; Agricultural communication; Agricultural policy; Good agricultural practice; Service (business)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008918237,0.001685728,0.001448758,0.004374824,0.001095587,0.002396255,0.002698374,0.001194357,0.0486752],"category_scores_gemma":[0.008172833,0.0008769429,0.001164079,0.01905585,0.0003813415,0.001954921,0.001425341,0.002294239,0.0492506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006673371,"about_ca_system_score_gemma":0.01069365,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6971909,"about_ca_topic_score_gemma":0.6883523,"domain_scores_codex":[0.9988143,0.00008846016,0.0001434347,0.0002756541,0.0004401214,0.0002379589],"domain_scores_gemma":[0.9930647,0.0004485823,0.000491204,0.0004591965,0.005115418,0.0004209891],"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":[0.00002246161,0.000008808684,0.00114047,0.0001891714,0.00001597079,0.000005205344,0.000009759079,0.0001109995,0.00001743827,0.0002093218,0.9973391,0.0009313516],"study_design_scores_gemma":[0.0001547601,0.00001407526,0.02089301,0.0005036829,0.00003833884,0.00002349559,0.0002326681,0.0003409716,0.0001869879,0.0005511848,0.9770172,0.00004369986],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004544129,0.00002119622,0.000009177252,0.00003155841,0.00001516818,0.00000359066,0.9996023,0.00002419381,0.0002473874],"genre_scores_gemma":[0.0002534418,0.00004687031,0.00007920208,0.00002325,0.000005977446,0.00002424937,0.9990251,0.00001657913,0.0005253345],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9513248,"threshold_uncertainty_score":0.6091849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03017723527684358,"score_gpt":0.2607753135966163,"score_spread":0.2305980783197727,"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."}}