{"id":"W3143159190","doi":"10.18280/ts.380125","title":"Analysis on Food Crispness Based on Time and Frequency Domain Features of Acoustic Signal","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"SIGNAL (programming language); Eigenvalues and eigenvectors; Waveform; Artificial neural network; Wavelet; Computer science; Amplitude; Time domain; Acoustics; Pattern recognition (psychology); Mathematics; Artificial intelligence; Telecommunications","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":[0.00004386146,0.0002030823,0.0003135981,0.0001754017,0.00003524952,0.00001672916,0.0001254286,0.0001078008,0.0004368039],"category_scores_gemma":[0.00002696273,0.0001899966,0.0001084755,0.0004705627,0.00006770636,0.00004053865,0.00001807499,0.0001913572,0.000005141342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000667506,"about_ca_system_score_gemma":0.000005630761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001803462,"about_ca_topic_score_gemma":0.000008528184,"domain_scores_codex":[0.9989712,0.00002253046,0.0002324228,0.0002592749,0.0002800221,0.0002346111],"domain_scores_gemma":[0.999454,0.0001923394,0.00004186301,0.0002130021,0.00004220386,0.00005665121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002653003,0.00008925574,0.0002003096,0.00005812522,0.000283855,0.00002817027,0.00003528714,0.2541318,0.7430335,0.0002916956,0.00009347727,0.001727932],"study_design_scores_gemma":[0.0008075934,0.000401377,0.003815998,0.0000725041,0.0003147525,0.000002744021,0.0001138836,0.03672407,0.9549935,0.00237292,0.00003266236,0.0003479932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768172,0.000215722,0.02092389,0.0001208874,0.00002239839,0.0001191694,0.0001162516,0.0003196861,0.001344793],"genre_scores_gemma":[0.9963808,0.000007445923,0.003424976,0.00006803177,0.0000271548,0.00001380955,0.00004091119,0.0000226531,0.00001419291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2174077,"threshold_uncertainty_score":0.7747838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006560111219085938,"score_gpt":0.205013676428945,"score_spread":0.198453565209859,"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."}}