{"id":"W4414306939","doi":"10.1016/j.ces.2025.122634","title":"Fast soft sensor model migration in injection molding processes: A reconstruction based deep learning framework","year":2025,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Injection Molding Process and Properties","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Soft sensor; Leverage (statistics); Process (computing); Deep learning; Transfer of learning; Production line; Production (economics); Molding (decorative); Process modeling","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":[0.0003981256,0.0007001261,0.0007283474,0.0003479698,0.000246393,0.00061769,0.001159981,0.001165111,0.001342218],"category_scores_gemma":[0.0009913604,0.0005432058,0.0005632694,0.0003869975,0.0004567622,0.001108583,0.0009513212,0.001499578,0.0003494609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00054498,"about_ca_system_score_gemma":0.0008396539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005563001,"about_ca_topic_score_gemma":0.004490152,"domain_scores_codex":[0.9998699,0.00002177269,0.000006922762,0.0000353124,0.00004003766,0.00002604175],"domain_scores_gemma":[0.9996523,0.0001367124,0.00006045793,0.00005453737,0.00006696896,0.00002912023],"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":[0.0001417422,0.00006936276,0.0006258953,0.00005684563,0.00002740431,0.00005488841,0.00003246803,0.9061273,0.01017353,0.002930748,0.0005510387,0.07920889],"study_design_scores_gemma":[8.691396e-7,0.000004588143,0.00002153651,7.976145e-7,8.452099e-7,0.000002772724,9.10873e-7,0.9990724,0.0006168769,0.0002301269,0.00004718149,9.679628e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03999427,0.0002956861,0.9575759,0.0001828806,0.00004599435,0.00002246606,0.00005207401,0.0008420855,0.0009886624],"genre_scores_gemma":[0.8469149,0.0002739446,0.1487321,0.000131547,0.00003600015,0.00005706973,0.0001875418,0.0001625322,0.003504328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005563001,"threshold_uncertainty_score":0.01106125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00680796835114899,"score_gpt":0.2093088766989331,"score_spread":0.2025009083477841,"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."}}