{"id":"W3092030728","doi":"10.1109/iemtronics51293.2020.9216421","title":"Light Spectra Optimization in Indoor Plant Growth for Internet of Things","year":2020,"lang":"en","type":"article","venue":"2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","topic":"Light effects on plants","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lactuca; Spinach; Spinacia; Environmental science; Humidity; Plant growth; Moisture; Light intensity; Materials science; Horticulture; Chemistry; Biology; Meteorology; Optics; Geography; Physics","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":[0.0001734636,0.000338081,0.00017215,0.00026673,0.0002144964,0.0003239661,0.0002391351,0.0002146044,0.001256043],"category_scores_gemma":[0.0002627386,0.00009807995,0.0002700149,0.0002317081,0.000131448,0.0002393213,0.0001983347,0.0001697149,0.0002324216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000268394,"about_ca_system_score_gemma":0.0001191574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008990356,"about_ca_topic_score_gemma":0.002312088,"domain_scores_codex":[0.9998397,0.00003532958,0.000004847315,0.0000288405,0.00005957977,0.00003166649],"domain_scores_gemma":[0.9997405,0.0001060316,0.00003504769,0.00002376377,0.00007373732,0.00002079511],"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":[0.0004531143,0.0001685478,0.006121996,0.0001968916,0.00002984366,0.0001327782,0.0001052035,0.01968798,0.9216673,0.0004888464,0.0003331437,0.05061439],"study_design_scores_gemma":[0.00008142737,0.001699517,0.08213044,0.00004030624,0.0001663196,0.0004644127,0.0004498002,0.09376075,0.8115349,0.0008605649,0.008732945,0.00007859088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9373918,0.0003757649,0.05440374,0.00005475767,0.00002299553,0.00003584953,0.00009373219,0.0005488333,0.007072545],"genre_scores_gemma":[0.9894965,0.00008157644,0.009583306,0.00002107354,0.000002982918,0.0000196175,0.0000531594,0.00005480571,0.0006869458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001256043,"threshold_uncertainty_score":0.004201889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788402710749965,"score_gpt":0.2182868832232778,"score_spread":0.2004028561157782,"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."}}