{"id":"W2501401573","doi":"10.1016/j.apacoust.2016.07.030","title":"Time domain identification and ranking of noise sources in a pneumatic nail gun","year":2016,"lang":"en","type":"article","venue":"Applied Acoustics","topic":"Advanced Sensor Technologies Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sandbox (software development); Noise (video); Acoustics; Engineering; Ranking (information retrieval); Acceleration; Noise floor; Noise measurement; Simulation; Computer science; Noise reduction; Physics; Artificial intelligence","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":[0.00012632,0.00008736803,0.0001460382,0.0001477289,0.00002042827,0.000009390633,0.0001402984,0.00007967548,0.00001315471],"category_scores_gemma":[0.00009526376,0.00007145722,0.00001080124,0.0001767193,0.0001330753,0.00004479779,0.00005235967,0.00009210465,0.00003280425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000531069,"about_ca_system_score_gemma":0.000004215635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.229038e-7,"about_ca_topic_score_gemma":0.000001273217,"domain_scores_codex":[0.999313,0.000006436556,0.0002258544,0.0001217148,0.0001441461,0.0001888219],"domain_scores_gemma":[0.9995071,0.0001936875,0.00003728857,0.0002216296,0.00001882214,0.00002145771],"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":[0.000005868735,0.000007912628,0.00005997508,0.0000940379,0.000007153545,0.000001724908,0.0001737455,0.008623719,0.9603373,0.0003755999,0.00003321316,0.03027974],"study_design_scores_gemma":[0.003920338,0.00007831072,0.01327188,0.0005743348,0.00005503334,0.0000192912,0.003044725,0.1978177,0.6970431,0.08242295,0.0006239053,0.001128424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167643,0.00007851472,0.0823458,0.00003035122,0.00001650076,0.0001801961,0.000007269612,0.0001782058,0.0003988566],"genre_scores_gemma":[0.9951552,0.0001548555,0.004574194,0.000002452169,0.000009630809,0.00002351324,0.000001452094,0.00002296864,0.00005576047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2632942,"threshold_uncertainty_score":0.2913941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007000714927190325,"score_gpt":0.2169205981120299,"score_spread":0.2099198831848396,"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."}}