{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003574506,0.0007167927,0.000425452,0.0003615563,0.0001705837,0.000485677,0.0005851898,0.0005716403,0.00151246],"category_scores_gemma":[0.0009116308,0.0002254774,0.0004048246,0.000723061,0.0001797489,0.0008666323,0.0003423727,0.0009601305,0.0002759305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004174001,"about_ca_system_score_gemma":0.0004052335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00752027,"about_ca_topic_score_gemma":0.006417154,"domain_scores_codex":[0.9998499,0.00002799241,0.00001298403,0.00004682931,0.00004301493,0.0000192665],"domain_scores_gemma":[0.9997912,0.00009505526,0.00003188003,0.00001527475,0.00005817603,0.000008505998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008821942,0.00008120437,0.00367782,0.0001975609,0.0001185638,0.00009558682,0.00002515222,0.8357532,0.003156341,0.005527429,0.004499525,0.1467794],"study_design_scores_gemma":[0.000001775021,0.00001214429,0.0003466927,0.00000577924,0.000005694136,0.000007873765,0.000003790668,0.9970242,0.0004046264,0.001638368,0.0005460124,0.000003003227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05849209,0.01018034,0.9194805,0.001387934,0.0006500469,0.0000678936,0.001168881,0.001352157,0.007220168],"genre_scores_gemma":[0.899758,0.007577075,0.08134095,0.0002332183,0.0002782297,0.0001212314,0.00151834,0.00006235033,0.009110631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00752027,"threshold_uncertainty_score":0.01495296,"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."}}