{"id":"W4396213970","doi":"10.2316/j.2023.203-0451","title":"A TIME-SERIES FORECASTING OF POWER CONSUMPTION AND FEATURE EXTRACTION IN AGRICULTURE SECTOR USING MACHINE LEARNING, 1-11.","year":2023,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Agriculture; Power consumption; Series (stratigraphy); Computer science; Feature (linguistics); Feature extraction; Time series; Consumption (sociology); Extraction (chemistry); Artificial intelligence; Industrial engineering; Agricultural engineering; Machine learning; Power (physics); Agricultural economics; Engineering; Economics; Geography; Social science; Sociology; Archaeology","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":[0.000230402,0.0001223198,0.0002069735,0.0002962753,0.00003191072,0.00005406337,0.0000718863,0.00009734737,0.00001795043],"category_scores_gemma":[0.00003991309,0.000100883,0.00004760165,0.0001212391,0.00002715009,0.0003106928,0.00002430882,0.0001771584,5.697202e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004822468,"about_ca_system_score_gemma":0.000009912726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001235082,"about_ca_topic_score_gemma":0.00007254795,"domain_scores_codex":[0.9991854,0.00003890951,0.0003451785,0.00008409527,0.0002246577,0.0001217846],"domain_scores_gemma":[0.9995231,0.00006780197,0.0001982991,0.00003182307,0.0001287809,0.00005016213],"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":[0.0001891498,0.00004555091,0.09483036,0.0002247771,0.0005130801,0.0003814994,0.003845077,0.7512354,0.1449527,0.0008198334,0.001132965,0.001829591],"study_design_scores_gemma":[0.002655272,0.0003728277,0.02742312,0.003667221,0.00008088561,0.008692401,0.00176667,0.8810005,0.007195797,0.00009034999,0.06628863,0.0007663564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952708,0.002293549,0.0004539983,0.00002999137,0.001474968,0.00001819174,0.00001326448,0.00003123221,0.0004140429],"genre_scores_gemma":[0.9988064,0.0003429201,0.0001506376,0.000004153334,0.0001777594,0.000001062342,0.00001336448,0.0000169354,0.0004867227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1377569,"threshold_uncertainty_score":0.4113888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387503723644809,"score_gpt":0.2268493834214167,"score_spread":0.2129743461849686,"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."}}