{"id":"W2686735702","doi":"10.1109/ccece.2017.7946787","title":"An IoT environmental data collection system for fungal detection in crop fields","year":2017,"lang":"en","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Environmental data; Computer science; Wind speed; Field (mathematics); Data collection; Internet of Things; Support vector machine; Process (computing); Relative humidity; Cloud computing; Real-time computing; Agricultural engineering; Machine learning; Meteorology; Embedded system; Engineering; Mathematics; Geography; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003293388,0.0003911871,0.0004653015,0.0008442554,0.0005322762,0.0005170716,0.0006214476,0.0004448042,0.003581677],"category_scores_gemma":[0.0004664395,0.0002378527,0.0002371109,0.0006591735,0.0001403073,0.0006786521,0.0003996182,0.0003021127,0.001179737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003009553,"about_ca_system_score_gemma":0.0004569969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001258564,"about_ca_topic_score_gemma":0.001860006,"domain_scores_codex":[0.9997203,0.00002846388,0.00002737648,0.00008241484,0.0001192199,0.0000222462],"domain_scores_gemma":[0.9996347,0.00006145686,0.0000346882,0.0000630458,0.000167827,0.00003829165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001409602,0.001080915,0.06163132,0.0007275761,0.0001587308,0.001560988,0.0006701905,0.02206346,0.3851129,0.004174976,0.03805506,0.4833543],"study_design_scores_gemma":[0.0003223855,0.001154679,0.08343978,0.0001928155,0.0002265743,0.001880743,0.0005199862,0.587603,0.2367407,0.003472949,0.08420794,0.0002386162],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3310437,0.0006361209,0.5912912,0.0009700475,0.0006233084,0.001302696,0.006468193,0.03231311,0.0353515],"genre_scores_gemma":[0.8584433,0.0003261443,0.1256733,0.0004736062,0.00008611893,0.0006816353,0.003073942,0.0001913551,0.01105057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003581677,"threshold_uncertainty_score":0.0119819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03301496235174523,"score_gpt":0.243487526595221,"score_spread":0.2104725642434758,"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."}}