{"id":"W2775985339","doi":"10.1515/aoa-2017-0068","title":"Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks","year":2017,"lang":"en","type":"article","venue":"Archives of Acoustics","topic":"Acoustic Wave Phenomena Research","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Physical Laboratory","keywords":"Artificial neural network; Partition (number theory); Gypsum; Computer science; Soundproofing; Structural engineering; Acoustics; Engineering; Materials science; Artificial intelligence; Mathematics; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003288538,0.0005627944,0.000343603,0.0003280556,0.0001294557,0.0003682611,0.0002147783,0.0004827791,0.000682918],"category_scores_gemma":[0.001116637,0.0002301272,0.0003100395,0.0002280922,0.0001792688,0.0002910946,0.0002045005,0.0003901496,0.0001544237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003541186,"about_ca_system_score_gemma":0.0002733243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004544239,"about_ca_topic_score_gemma":0.003191318,"domain_scores_codex":[0.9998789,0.00003950279,0.000006912651,0.00002650426,0.00003299861,0.00001516876],"domain_scores_gemma":[0.9995803,0.0002724603,0.00004217263,0.00001824726,0.00007512772,0.0000116932],"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":[0.00004339985,0.00002800216,0.001654222,0.00001439702,0.00001567934,0.00002373828,0.000006820481,0.9848898,0.003561965,0.00006406361,0.00007845376,0.009619555],"study_design_scores_gemma":[6.133687e-7,0.000007864998,0.0004490028,9.949227e-7,0.000001507405,0.000001671713,0.000001590284,0.9987066,0.0007827649,0.0000331661,0.00001313675,0.000001068547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7230492,0.0002006968,0.2743647,0.0000766564,0.00003891506,0.00002429983,0.0001280344,0.0003964237,0.001721137],"genre_scores_gemma":[0.989109,0.00004344931,0.01016928,0.000005458574,0.000003657562,0.00001594263,0.00006091956,0.000005876536,0.0005863348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004544239,"threshold_uncertainty_score":0.009035528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03409794605301629,"score_gpt":0.2546089886721166,"score_spread":0.2205110426191003,"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."}}