{"id":"W3135588926","doi":"10.3390/su13052521","title":"Expert Insights on the Impacts of, and Potential for, Agricultural Big Data","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Big data; Software deployment; Data science; Agriculture; Field (mathematics); Productivity; Agricultural productivity; Analytics; Computer science; Knowledge management; Business; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002033303,0.0001161788,0.0001496473,0.000003259198,0.0002323329,0.0000573695,0.000272241,0.0000712564,0.00002718722],"category_scores_gemma":[0.0007269937,0.00002743764,0.00007153489,0.0002359717,0.00009542268,0.0001038505,0.0002416798,0.00008021457,7.557486e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003549691,"about_ca_system_score_gemma":0.00003649203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000169899,"about_ca_topic_score_gemma":0.000510013,"domain_scores_codex":[0.9990292,0.0001036926,0.0001596348,0.0003465101,0.0001511517,0.0002097735],"domain_scores_gemma":[0.9988767,0.0003492222,0.00005653857,0.0001787541,0.0004713917,0.0000674218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003994262,0.001545085,0.01570839,0.0002315093,0.0001878134,0.00004664865,0.003002377,0.00001013975,0.5379418,0.04013472,0.06758813,0.333204],"study_design_scores_gemma":[0.0002616514,0.0003630596,0.8722999,0.00002089914,0.00002772007,0.00001541397,0.0111633,0.00002708056,0.02099281,0.01452105,0.08003757,0.0002695119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982035,0.0004819733,0.000003610249,0.01680869,0.0001307104,0.0003192122,0.00005638286,0.00001840727,0.0001460472],"genre_scores_gemma":[0.9987459,0.00004422501,0.00001367799,0.0003347351,0.0005114166,0.00001554404,0.0001838521,4.408341e-7,0.0001502678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8565915,"threshold_uncertainty_score":0.178694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03181802362436519,"score_gpt":0.2531007135882223,"score_spread":0.2212826899638571,"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."}}