{"id":"W3019913886","doi":"10.3390/app10082870","title":"An Unsupervised Regularization and Dropout based Deep Neural Network and Its Application for Thermal Error Prediction","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Department of Education of Liaoning Province","keywords":"Computer science; Artificial neural network; Artificial intelligence; Heavy duty; Regularization (linguistics); Machine learning; Compensation (psychology); Dropout (neural networks); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008429651,0.0006438611,0.0005345357,0.0003663198,0.000328892,0.0004178159,0.001059885,0.0008367451,0.0006991139],"category_scores_gemma":[0.001458884,0.0003579383,0.000579704,0.0005070582,0.0004641272,0.0009360714,0.0007156146,0.001260717,0.000173836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008396255,"about_ca_system_score_gemma":0.001057231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008008394,"about_ca_topic_score_gemma":0.006809489,"domain_scores_codex":[0.9996881,0.00005909977,0.00001994284,0.00009111225,0.0001068329,0.00003494747],"domain_scores_gemma":[0.9995994,0.0001277118,0.00006096493,0.00004636525,0.0001464467,0.00001907892],"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":[0.0001346152,0.0001002702,0.001621022,0.00008564983,0.00005990612,0.0000899311,0.00006272231,0.853309,0.01171539,0.004936784,0.001779539,0.1261051],"study_design_scores_gemma":[9.344632e-7,0.000006742144,0.00007353316,0.000001241161,0.000001943814,0.00000337153,8.193482e-7,0.9988788,0.0006955195,0.0002417262,0.00009342485,0.000001959998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02828258,0.0004856215,0.968922,0.0002230871,0.00005740836,0.00002866789,0.00006806147,0.0006903113,0.001242304],"genre_scores_gemma":[0.8451001,0.0006023663,0.148162,0.0001779069,0.00007217893,0.0001298777,0.0003227332,0.00009054683,0.00534235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008008394,"threshold_uncertainty_score":0.01592356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411537615629635,"score_gpt":0.2332238921860706,"score_spread":0.2191085160297743,"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."}}