{"id":"W2154090733","doi":"10.1109/ccece.2006.277698","title":"An Automatic System for Crackles Detection and Classification","year":2006,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Crackles; Computer science; Speech recognition; Filter (signal processing); Wavelet; Pattern recognition (psychology); Noise reduction; Noise (video); Artificial intelligence; SIGNAL (programming language); Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001112788,0.00003805237,0.00004394465,0.00003055783,0.0001224239,0.0001798557,0.00008854423,0.00002186875,7.816693e-7],"category_scores_gemma":[0.000003134406,0.00003105582,0.000008892739,0.00006847733,0.000009371504,0.0003880525,0.000008903996,0.00001273856,0.000003226382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000159688,"about_ca_system_score_gemma":0.00001001708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001428486,"about_ca_topic_score_gemma":0.00001516726,"domain_scores_codex":[0.999638,0.00001109001,0.00008948124,0.0001407015,0.00005196632,0.00006873004],"domain_scores_gemma":[0.9997703,0.00002022446,0.00004409474,0.0001171533,0.00003039362,0.00001785045],"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":[0.00000114958,0.00002573646,0.0002330623,0.0002193489,0.000002114861,3.126115e-7,0.0001187403,0.00003063819,0.08713916,0.09972747,0.0002977933,0.8122045],"study_design_scores_gemma":[0.00008806499,0.0000223738,0.009971179,0.00001368421,0.000002371801,0.000007264867,0.00004435104,0.973635,0.01397581,0.001873282,0.0003137249,0.00005294038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08491374,0.00002001958,0.9129127,0.0001613359,0.00007116602,0.00008013487,1.471443e-7,0.0002675488,0.001573265],"genre_scores_gemma":[0.9353887,1.768658e-7,0.06439894,0.0000459694,0.00005393826,0.00001623216,6.25996e-7,0.00000229471,0.00009311884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9736043,"threshold_uncertainty_score":0.1734353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779812891195221,"score_gpt":0.2449829665597346,"score_spread":0.2271848376477824,"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."}}