{"id":"W4402983900","doi":"10.1016/j.jspr.2024.102427","title":"Advanced hybrid empirical mode decomposition, convolutional neural network and long short-term memory neural network approach for predicting grain pile humidity based on meteorological inputs","year":2024,"lang":"en","type":"article","venue":"Journal of Stored Products Research","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Humidity; Term (time); Artificial neural network; Convolutional neural network; Pile; Mode (computer interface); Environmental science; Hilbert–Huang transform; Decomposition; Computer science; Meteorology; Artificial intelligence; Algorithm; Ecology; Biology; Geography; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036164,0.0002307284,0.0003645391,0.0001311684,0.0006380028,0.0001522802,0.0002871891,0.0001150207,0.00001878922],"category_scores_gemma":[0.0004408337,0.0001696812,0.0001427891,0.0005244369,0.0004730441,0.0003343066,0.0001936999,0.001280252,0.000002149856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003568819,"about_ca_system_score_gemma":0.000106631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000105766,"about_ca_topic_score_gemma":0.00001321696,"domain_scores_codex":[0.99617,0.0006998033,0.0005253887,0.0006328204,0.001188802,0.0007832085],"domain_scores_gemma":[0.9984502,0.0007405251,0.0001094973,0.0002373269,0.0001798074,0.0002826801],"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":[0.001808377,0.0002692131,0.04571518,0.0001668498,0.00008679611,0.0004239527,0.000262742,0.8680674,0.006712767,0.00001142908,0.02926491,0.04721033],"study_design_scores_gemma":[0.0006384685,0.00118632,0.1537327,0.0001446369,0.00004617962,0.0008525198,0.00003813494,0.8409774,0.0009308389,0.0005985146,0.0005962619,0.0002579508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885764,0.001680179,0.004678697,0.002972946,0.0009314357,0.0006902959,0.000007444276,0.00004327546,0.0004193365],"genre_scores_gemma":[0.9839798,0.0000283674,0.01282354,0.0001656766,0.002861085,0.000007270118,0.00003354283,0.00003185064,0.00006888893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1080176,"threshold_uncertainty_score":0.6919397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05992734501519527,"score_gpt":0.3774087595773362,"score_spread":0.317481414562141,"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."}}