{"id":"W2801031074","doi":"10.1139/tcsme-2010-0002","title":"NUMERICAL ASSESSMENT OF REVERSE-FLOW MUFFLERS USING A SIMULATED ANNEALING METHOD","year":2010,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Acoustic Wave Phenomena Research","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Muffler; Simulated annealing; Transmission loss; Acoustics; Noise control; Noise reduction; Maximization; Computer science; Broadband; Noise (video); Mathematical optimization; Algorithm; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008362493,0.0004828916,0.0005654589,0.0005206369,0.0004130079,0.0005896703,0.0005430955,0.0009735827,0.001792202],"category_scores_gemma":[0.002081792,0.0004012503,0.0006801747,0.0002805697,0.0006228983,0.0003526465,0.0004364421,0.000431698,0.0002114079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006206574,"about_ca_system_score_gemma":0.0007276096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003242009,"about_ca_topic_score_gemma":0.0021115,"domain_scores_codex":[0.9997872,0.00008791743,0.000008653404,0.00002395873,0.00006668972,0.00002550518],"domain_scores_gemma":[0.9991122,0.0005891561,0.00008137035,0.00005120881,0.0001404755,0.00002560353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002516504,0.00001098784,0.0002669617,0.00002409066,0.000007315563,0.00001694936,0.00002510669,0.9930507,0.002718555,0.001641671,0.00004429041,0.002168225],"study_design_scores_gemma":[0.000003023836,0.00001445854,0.0000567569,0.000002042922,0.000002216399,0.00000236984,0.000003958201,0.9991136,0.0005409362,0.0001481604,0.0001105673,0.000001861901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2832515,0.0002956402,0.7030084,0.0002417556,0.00004016009,0.0001171765,0.00009010085,0.0004257222,0.01252961],"genre_scores_gemma":[0.8768572,0.0001109983,0.1209453,0.00002103381,0.00000766423,0.0001424565,0.00005795676,0.00004445586,0.001812979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003242009,"threshold_uncertainty_score":0.006446302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973731246281238,"score_gpt":0.2871331557365346,"score_spread":0.2673958432737222,"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."}}