{"id":"W4287879026","doi":"10.3390/su14159120","title":"Big Data Privacy in Smart Farming: A Review","year":2022,"lang":"en","type":"review","venue":"Sustainability","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Big data; Agriculture; Variety (cybernetics); Service provider; Information privacy; Computer science; Business; Internet privacy; Privacy policy; Privacy by Design; Data security; Computer security; Data science; Service (business); Marketing; Encryption; Data mining","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":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.005912654,0.0005489266,0.002098455,0.0003280816,0.0002385208,0.0001672975,0.008591017,0.0001635668,0.00001963722],"category_scores_gemma":[0.005167644,0.0004933506,0.0003990356,0.002756143,0.00007791615,0.0003998574,0.01448679,0.00123344,0.00002373061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002308226,"about_ca_system_score_gemma":0.005225722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001686405,"about_ca_topic_score_gemma":0.000004675865,"domain_scores_codex":[0.993969,0.001381397,0.001316566,0.001923316,0.000527708,0.0008820615],"domain_scores_gemma":[0.9912338,0.0008018906,0.0005062896,0.007119494,0.0001887265,0.0001498232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[4.072016e-7,0.00007974684,0.00002409922,0.09586214,0.00001278064,0.00009467715,0.00009996869,7.74743e-8,2.925e-10,0.0001720183,0.007271819,0.8963823],"study_design_scores_gemma":[0.00006868673,0.00002600721,0.00001376226,0.007483964,0.00008135563,0.00004163955,0.000006882007,0.0001436743,5.127775e-9,0.001778304,0.9898771,0.0004786394],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.484053e-7,0.9876924,0.002620245,0.0006744618,0.005995683,0.002275601,0.000003286722,0.000189103,0.0005486328],"genre_scores_gemma":[6.208443e-7,0.9975188,0.000788236,0.0002088585,0.000851215,0.0001901184,0.0001565934,0.00003333742,0.000252165],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9826053,"threshold_uncertainty_score":0.9997518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2085949636568012,"score_gpt":0.397169604127399,"score_spread":0.1885746404705978,"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."}}