{"id":"W4381856792","doi":"10.1007/s10845-023-02153-w","title":"Production quality prediction of cross-specification products using dynamic deep transfer learning network","year":2023,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transfer of learning; Adaptability; Quality (philosophy); Domain (mathematical analysis); Process (computing); Artificial intelligence; Production (economics); Domain adaptation; Data mining; Machine learning; Mathematics","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":[0.00260692,0.0001415579,0.0002400056,0.0003289857,0.0002070415,0.0001351492,0.0004541797,0.00007293467,0.000009045171],"category_scores_gemma":[0.0004088268,0.0001275622,0.000109049,0.0005483246,0.00004688744,0.000962317,0.00006426585,0.0004749138,0.00001320086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001463984,"about_ca_system_score_gemma":0.00006162361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001228461,"about_ca_topic_score_gemma":0.000002813154,"domain_scores_codex":[0.9976529,0.0002634693,0.0009974228,0.0003179774,0.0005270247,0.0002412198],"domain_scores_gemma":[0.9984135,0.00009783012,0.0006564877,0.0003890279,0.0003802515,0.00006287696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006818117,0.00006995989,0.007935832,0.0001576038,0.00005704175,0.00000376351,0.001491519,0.8461118,0.04329081,0.0006858773,0.00004978112,0.1000779],"study_design_scores_gemma":[0.0002687306,0.0001914473,0.3659861,0.0002623772,0.00003915212,0.0001221027,0.0002835574,0.2789342,0.3488352,0.0009447798,0.003875294,0.0002569773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.528519,0.0001002509,0.4700271,0.0002779512,0.0008710884,0.00009769816,7.978232e-7,0.00007368984,0.00003235763],"genre_scores_gemma":[0.9922274,0.0003280818,0.006856929,0.000006475791,0.0004039786,0.000001933949,0.00001805409,0.00001458195,0.0001426141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5671775,"threshold_uncertainty_score":0.5201837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06617760022358855,"score_gpt":0.3334831305166272,"score_spread":0.2673055302930387,"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."}}