{"id":"W2475266777","doi":"10.1177/2053951716648174","title":"Big Data in food and agriculture","year":2016,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Agriculture, Land Use, Rural Development","field":"Agricultural and Biological Sciences","cited_by":418,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; St. Thomas University","funders":"","keywords":"Big data; Scholarship; Agriculture; Data science; Affordance; Computer science; Economics; Economic growth; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003744623,0.0002158425,0.0002101769,0.000003706255,0.0001663245,0.00008734554,0.001398171,0.0001873923,0.00003604711],"category_scores_gemma":[0.00007729531,0.00005147095,0.00004203008,0.0003661614,0.0000716479,0.0004084909,0.002006962,0.0001322052,0.00003796554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000378958,"about_ca_system_score_gemma":0.0000161687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002105714,"about_ca_topic_score_gemma":0.005107466,"domain_scores_codex":[0.9982331,0.00005078554,0.0002459526,0.0007990229,0.0002854086,0.0003856955],"domain_scores_gemma":[0.9992086,0.0001412383,0.00008544357,0.0003902967,0.00003870053,0.00013572],"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":[0.00001133086,0.0001185553,0.02149583,0.00001527558,0.000057294,0.000004560499,0.0002123624,3.349282e-8,0.02147539,0.0001941252,0.3806371,0.5757781],"study_design_scores_gemma":[0.0003321065,0.00005969278,0.5574281,0.00006097126,0.00001153334,0.00001129341,0.0005321628,0.00000416104,0.0004310072,0.0006785264,0.4401362,0.0003142343],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904938,0.001166723,0.00001438451,0.003864276,0.0003633469,0.0002691146,0.003561438,0.00008376261,0.0001831665],"genre_scores_gemma":[0.9900789,0.002608757,0.0003792942,0.000663093,0.001269452,0.00001233282,0.004420417,0.000001596381,0.0005661439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5754639,"threshold_uncertainty_score":0.2850086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1138633693590624,"score_gpt":0.2399007308208972,"score_spread":0.1260373614618348,"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."}}