{"id":"W3104475691","doi":"10.1109/iecon43393.2020.9254993","title":"Development of an Intelligent LED Lighting Control Testbed for IoT-based Smart Greenhouses","year":2020,"lang":"en","type":"article","venue":"IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society","topic":"Light effects on plants","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Testbed; Greenhouse; Computer science; LED lamp; Internet of Things; Real-time computing; Smart lighting; Control (management); Embedded system; Automotive engineering; Engineering; Architectural engineering; Artificial intelligence; Electrical engineering; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004408951,0.0003493017,0.0004431113,0.0002613717,0.0002781887,0.0004839748,0.00101982,0.0003262874,0.00195454],"category_scores_gemma":[0.0004094427,0.0001561321,0.0002438979,0.0001160255,0.0002690602,0.000729748,0.0005517593,0.00035959,0.0005296514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004014611,"about_ca_system_score_gemma":0.0005788518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007888591,"about_ca_topic_score_gemma":0.0006938762,"domain_scores_codex":[0.9997153,0.00003709356,0.00001751776,0.0000685983,0.0001136904,0.00004776144],"domain_scores_gemma":[0.9997618,0.00003432049,0.00002958854,0.0000479991,0.00006597656,0.00006037432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003566535,0.0005835219,0.005449261,0.0004303246,0.00004163108,0.0006406431,0.00024858,0.04211461,0.8779712,0.004703671,0.002324657,0.06513514],"study_design_scores_gemma":[0.0002187239,0.001952681,0.01458103,0.00009124444,0.00007276036,0.0006987177,0.000259006,0.3634408,0.5716793,0.00179485,0.04509858,0.0001123823],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2758609,0.0002176475,0.7038547,0.0002019007,0.0002164344,0.0009732346,0.0004302823,0.01081773,0.007427068],"genre_scores_gemma":[0.7379723,0.0001451558,0.2560114,0.00007073326,0.00001999603,0.0006580994,0.0005189427,0.0001647673,0.004438485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00195454,"threshold_uncertainty_score":0.00653851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05931428662350566,"score_gpt":0.2407779909192151,"score_spread":0.1814637042957095,"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."}}