{"id":"W3019553070","doi":"10.1109/smartnets48225.2019.9069766","title":"Wireless Sensor Network and Deep Learning For Prediction Greenhouse Environments","year":2019,"lang":"en","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Greenhouse; Mean squared error; Atmosphere (unit); Dew point; Wireless sensor network; Computer science; Artificial neural network; Atmospheric model; Environmental science; Greenhouse gas; Recurrent neural network; Meteorology; Humidity; Real-time computing; Machine learning; Statistics; Mathematics; Geography","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":[0.0001166925,0.0000803841,0.0001076895,0.000003795565,0.0001582955,0.00001473727,0.00006616685,0.0001256174,0.0002353506],"category_scores_gemma":[0.000007765336,0.00003062908,0.00003317277,0.0000479933,0.00003026229,0.00005815981,0.00003568445,0.00008504394,0.0000606709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006748446,"about_ca_system_score_gemma":4.540214e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001366991,"about_ca_topic_score_gemma":0.0001193043,"domain_scores_codex":[0.9994026,0.00002116803,0.0001073677,0.0002013795,0.00005513915,0.0002123406],"domain_scores_gemma":[0.999777,0.0001078429,0.00004370985,0.00003048442,0.000006187712,0.00003482911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007463894,0.00003292291,0.6936877,0.000006431505,0.00002514201,7.39611e-7,0.0000213665,0.0000953363,0.124958,0.0007907793,0.0001036999,0.1802032],"study_design_scores_gemma":[0.0007553205,0.0008734781,0.9282874,0.00001320637,0.00003142659,0.000009593198,0.0004467907,0.01185887,0.001008539,0.0006816353,0.05579885,0.0002348245],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998511,0.00008908059,0.0001540301,0.0003611037,0.00006349885,0.0003062925,0.000005690367,0.0001701949,0.0003390936],"genre_scores_gemma":[0.9976191,0.0001264584,0.00009072117,0.0001310522,0.00008889339,0.00002374784,0.00002531235,0.000001037792,0.001893663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2345997,"threshold_uncertainty_score":0.2576925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007756224248546301,"score_gpt":0.1776142616067682,"score_spread":0.1698580373582219,"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."}}