{"id":"W2008729476","doi":"10.1021/ie061348r","title":"Use of Wavelet Packet Transform in Characterization of Surface Quality","year":2007,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Wavelet packet decomposition; Discrete wavelet transform; Second-generation wavelet transform; Wavelet; Wavelet transform; Stationary wavelet transform; Pattern recognition (psychology); Lifting scheme; Computer science; Harmonic wavelet transform; Artificial intelligence; Feature extraction; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001550294,0.0001629655,0.0004156449,0.0001659357,0.00002439696,0.00002041182,0.0002784055,0.0004440857,0.0005352333],"category_scores_gemma":[0.001156337,0.000177683,0.00009357523,0.00153914,0.000103236,0.0001298545,0.0000497272,0.0008765004,0.000001224277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002122356,"about_ca_system_score_gemma":0.0001250458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003140786,"about_ca_topic_score_gemma":0.000003917937,"domain_scores_codex":[0.9978145,0.00001777442,0.0007331428,0.0002649183,0.0006588524,0.0005108009],"domain_scores_gemma":[0.9985605,0.0006341071,0.0001245381,0.0003529217,0.0002074019,0.0001205125],"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":[0.0001612403,0.000131745,0.005285094,0.0003594381,0.00004700117,0.000006298817,0.00008470302,0.0001669835,0.9930884,0.00002338399,0.00001702225,0.0006286394],"study_design_scores_gemma":[0.0006004035,0.00001424673,0.001470302,0.00009265557,0.00001253422,0.000001887366,0.0001141509,0.0002452715,0.9966648,0.000005626248,0.0006319336,0.0001461638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981067,0.0000306627,0.00007387144,0.0000510725,0.0000241017,0.00007719301,0.00009646855,0.00003003334,0.001509925],"genre_scores_gemma":[0.9989025,0.00003766281,0.00004632937,0.000001112892,0.00015356,0.000003582817,0.0001004128,0.00002362136,0.0007312379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003814792,"threshold_uncertainty_score":0.7245701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2064984234298309,"score_gpt":0.3939064944857694,"score_spread":0.1874080710559386,"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."}}