{"id":"W4390148919","doi":"10.23977/jeis.2023.080604","title":"An intelligent plant growth monitoring system based on ESP32 and IoT resource explorer platform","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mobile phone; Computer science; Microcontroller; Process (computing); Cloud computing; Resource (disambiguation); Embedded system; Internet of Things; Real-time computing; Telecommunications; Computer network; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009803843,0.00007718347,0.0001022348,0.0000872326,0.0003102241,0.0002611853,0.0001920185,0.00003523191,0.000002381557],"category_scores_gemma":[0.00004690702,0.00002697425,0.00002637962,0.000645357,0.0000473078,0.001488722,0.00002525419,0.0001353491,0.00000422029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005829204,"about_ca_system_score_gemma":0.00003071099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004042238,"about_ca_topic_score_gemma":0.00000174538,"domain_scores_codex":[0.9989876,0.00001163982,0.0002722915,0.00007933124,0.0004267545,0.0002223667],"domain_scores_gemma":[0.9994089,0.0000754257,0.000193563,0.00002885797,0.0001542617,0.0001389888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008534818,0.0002510695,0.04137005,0.000207107,0.00004704927,0.00003250124,0.008828421,0.005102644,0.3691737,0.0797414,0.003960636,0.4904319],"study_design_scores_gemma":[0.001061968,0.00916417,0.5720614,0.0006996381,0.00004400788,0.0004100625,0.02749204,0.1011163,0.1357211,0.0006998979,0.1506397,0.0008896512],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989152,0.00005927883,0.0000166249,0.0004476732,0.0001006842,0.00005624686,0.000003615808,0.00002110015,0.0003795948],"genre_scores_gemma":[0.9994768,0.0001979221,0.00003400282,0.0001408147,0.0001407796,0.00000141972,0.000005016839,3.194821e-7,0.000002932441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5306913,"threshold_uncertainty_score":0.2518616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997770296778437,"score_gpt":0.2241378498402735,"score_spread":0.2041601468724892,"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."}}