{"id":"W4313905527","doi":"10.2316/j.2022.201-0268","title":"A NOVEL EMD-IABC BASED DE-NOISING FOR GRAIN IMPACT SIGNAL, 197-204.","year":2022,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003113713,0.0005966164,0.0004847636,0.0006057001,0.0005578346,0.0008680508,0.0007632654,0.0005980087,0.005465442],"category_scores_gemma":[0.0005887715,0.0001839851,0.0002380442,0.0005602164,0.0003510649,0.0006283296,0.0005238047,0.0005733554,0.002100235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004098872,"about_ca_system_score_gemma":0.0006336621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002049669,"about_ca_topic_score_gemma":0.006305171,"domain_scores_codex":[0.9997022,0.00003476549,0.00001255096,0.00005181298,0.000165083,0.00003367021],"domain_scores_gemma":[0.9997925,0.00003182906,0.00001609746,0.00003622738,0.0001067403,0.00001656026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007975458,0.0001938947,0.001106115,0.000253065,0.00006490221,0.0002184425,0.00009290264,0.01118848,0.4109811,0.006801311,0.00621619,0.562086],"study_design_scores_gemma":[0.0001422301,0.0006550361,0.003969227,0.0000733049,0.0001246726,0.001186116,0.0001356193,0.5468911,0.3933693,0.002749587,0.05060979,0.00009403832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0527095,0.001962639,0.9182531,0.0004855576,0.0007186279,0.0002425507,0.0003185373,0.002256783,0.02305264],"genre_scores_gemma":[0.3835993,0.0008641907,0.5798249,0.0004915631,0.000187793,0.0001377151,0.000597097,0.0001117243,0.03418579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005465442,"threshold_uncertainty_score":0.01828372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007536101542071895,"score_gpt":0.2275215089158236,"score_spread":0.2199854073737517,"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."}}