{"id":"W4322716928","doi":"10.48550/arxiv.2302.13317","title":"TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New Brunswick Innovation Foundation","keywords":"Computer science; Classifier (UML); Transfer of learning; Artificial intelligence; Bounding overwatch; Machine learning; Labeled data; Pattern recognition (psychology); Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005395284,0.0004486905,0.0005268134,0.001017452,0.0001625242,0.00008530683,0.0002343429,0.0009765027,0.00001087977],"category_scores_gemma":[0.00001211512,0.0005534717,0.0003688862,0.0007020445,0.00003250031,0.0002086003,0.00008298644,0.001619198,0.00002536357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006786184,"about_ca_system_score_gemma":0.00003808039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009815444,"about_ca_topic_score_gemma":0.000197875,"domain_scores_codex":[0.9980663,0.000240097,0.0003986953,0.0007342582,0.0001116136,0.0004490176],"domain_scores_gemma":[0.9993999,0.00006445051,0.00004838719,0.0003498768,0.00004063183,0.00009673973],"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":[0.00004991933,0.00001739333,0.0003661858,0.0003308362,0.00009751418,0.00008287188,0.0001799721,0.9913142,0.00637096,0.00002328886,0.000004477048,0.001162347],"study_design_scores_gemma":[0.0005190919,0.00005139047,0.0008032355,0.0003443825,0.0001092387,0.00001502827,0.0002430679,0.9353654,0.06164446,0.0001497882,0.0001340729,0.0006208367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7096268,0.0000193035,0.2844352,6.833898e-7,0.0006050228,0.000542687,0.000007992943,0.004182772,0.0005795717],"genre_scores_gemma":[0.9994028,0.00007908854,0.0001153502,0.000001881273,0.0001332573,0.000007621152,0.00002225162,0.0001247091,0.0001130605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.289776,"threshold_uncertainty_score":0.9996917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08780876397727855,"score_gpt":0.196788938321637,"score_spread":0.1089801743443585,"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."}}