{"id":"W1973699074","doi":"10.1142/s0219878913500149","title":"HILBERT–HUANG TRANSFORM FOR FEATURE EXTRACTION OF TEMPERATURE MODULATED MOS SENSORS","year":2013,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Centres of Excellence","keywords":"Hilbert–Huang transform; Computer science; Hilbert transform; Fingerprint (computing); Feature extraction; Pattern recognition (psychology); Set (abstract data type); Feature (linguistics); Modulation (music); Artificial intelligence; Biological system; Acoustics; Spectral density; Telecommunications; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00005355936,0.0001101135,0.0001498967,0.0002825553,0.00002092225,0.00005109795,0.0001821212,0.0001734983,0.00007915075],"category_scores_gemma":[0.00007805908,0.00009722513,0.0001159853,0.0001131903,0.00002286596,0.002484098,0.000006682442,0.0002026804,0.00001026769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001742361,"about_ca_system_score_gemma":0.000005544684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002568841,"about_ca_topic_score_gemma":1.865734e-7,"domain_scores_codex":[0.9990067,0.000004618837,0.0005369618,0.00003870807,0.0003088981,0.0001040977],"domain_scores_gemma":[0.9985391,0.0000536691,0.0002991509,0.00006711118,0.001005386,0.00003554129],"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":[0.00009628063,0.00002376042,0.00001941322,0.0000612738,0.0001576441,0.000001262294,0.0002578218,0.1166826,0.8451015,0.0003775882,0.004783234,0.03243761],"study_design_scores_gemma":[0.0009080343,0.00006711142,0.001152366,0.0001024745,0.00001767615,0.000108946,0.0003138269,0.03399798,0.9577318,0.002636829,0.002824265,0.0001386341],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414006,0.0000880779,0.05507479,0.001473843,0.0007894519,0.0003132259,0.00008378158,0.0001455348,0.0006306637],"genre_scores_gemma":[0.9925483,0.00009022286,0.007024466,0.00009014684,0.0001054826,0.000009997346,0.0001011383,0.00001033917,0.0000199034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1126304,"threshold_uncertainty_score":0.3964726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003694583729650907,"score_gpt":0.2256488937799375,"score_spread":0.2219543100502866,"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."}}