{"id":"W4315473669","doi":"10.1109/jsen.2023.3234194","title":"E-Nose System Based on Fourier Series for Gases Identification and Concentration Estimation From Food Spoilage","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Ministry of Natural Resources; China Geological Survey; Shanghai Jiao Tong University; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Electronic nose; Food spoilage; Overfitting; Computer science; Convolutional neural network; Time series; Artificial intelligence; Biological system; Artificial neural network; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00007836583,0.000135983,0.000144054,0.00009050086,0.0001324088,0.00009987045,0.00007215838,0.0001040869,0.000003762683],"category_scores_gemma":[0.0001940608,0.0001322682,0.00004381362,0.0001510407,0.00004184604,0.0002427645,0.000004150804,0.0001542939,0.00001452994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190151,"about_ca_system_score_gemma":0.000004916173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.127191e-7,"about_ca_topic_score_gemma":0.000001240704,"domain_scores_codex":[0.9991888,0.00001698625,0.0002756106,0.000145382,0.0001710871,0.0002021451],"domain_scores_gemma":[0.9994466,0.0001971164,0.00009182403,0.0001451493,0.00006174373,0.00005759738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005798643,0.000007487498,0.00004991565,0.00009631435,0.00002792958,0.0000147972,0.00009432989,0.5702335,0.4236009,0.0001939554,0.001325367,0.004297583],"study_design_scores_gemma":[0.0003304425,0.0000516476,0.0002373157,0.00008502011,0.00001828424,0.0000185611,0.0002360854,0.4481011,0.5496596,0.0009335396,0.0002158456,0.0001125349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608343,0.000049194,0.03721954,0.0001883116,0.0005068046,0.0001937004,0.00009319791,0.0008684354,0.00004657109],"genre_scores_gemma":[0.9954842,0.0000519083,0.00418045,0.000009497966,0.0001547358,0.00001703142,0.00003870548,0.00003210832,0.00003135892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1260587,"threshold_uncertainty_score":0.5393741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405614939480371,"score_gpt":0.2292589187135022,"score_spread":0.2152027693186985,"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."}}