{"id":"W4403863359","doi":"10.1109/tii.2024.3463705","title":"Multi-Source Domain Generalization for Machine Remaining Useful Life Prediction via Risk Minimization-Based Test-Time Adaptation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Generalization; Minification; Adaptation (eye); Domain adaptation; Machine learning; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007088832,0.0003388272,0.0003189872,0.0006605632,0.0004280045,0.0002874308,0.0001079848,0.0006255427,0.00007842621],"category_scores_gemma":[0.0001480838,0.000341519,0.000213771,0.0008368675,0.00003010249,0.0006324898,0.000001038413,0.0006042831,0.00009839914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031635,"about_ca_system_score_gemma":0.0001498893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005709374,"about_ca_topic_score_gemma":0.00001575221,"domain_scores_codex":[0.9977862,0.0000955088,0.001208495,0.0002021801,0.0003923138,0.0003152556],"domain_scores_gemma":[0.9985422,0.0006839713,0.0001945934,0.0002631507,0.0001486741,0.0001673587],"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":[0.0001066826,0.00004129838,0.00001115813,0.0001033338,0.00008144922,3.771518e-7,0.001060752,0.9671172,0.0008594263,0.000002762191,0.001415398,0.0292001],"study_design_scores_gemma":[0.002327292,0.0003261383,0.000006112692,0.0002078015,0.0001544387,0.000006240544,0.0002853941,0.9781754,0.009046584,0.000007995973,0.009145113,0.0003115143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01724027,0.00002463572,0.9752463,0.00003497114,0.003921845,0.001249056,0.0009128763,0.001302337,0.00006768561],"genre_scores_gemma":[0.9829381,0.0000268307,0.01499763,0.00008577355,0.000929367,0.0003113479,0.0002729602,0.0001301264,0.0003079344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9656978,"threshold_uncertainty_score":0.9999037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03353389467160457,"score_gpt":0.2317651529088914,"score_spread":0.1982312582372868,"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."}}