{"id":"W3193440158","doi":"10.1007/978-3-030-75506-5_64","title":"Deep Learning-Based Automatic Detection of Defective Tablets in Pharmaceutical Manufacturing","year":2021,"lang":"en","type":"book-chapter","venue":"World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Convolutional neural network; Hyperparameter; Transfer of learning; Deep learning; Machine learning; Pattern recognition (psychology); Set (abstract data type); AdaBoost","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":[0.0004938041,0.0006461001,0.0006919918,0.00111272,0.0001551052,0.0006895833,0.001057009,0.0008628828,0.001410142],"category_scores_gemma":[0.0008475205,0.0004008458,0.0006093145,0.0005833402,0.000216561,0.0005914803,0.0006446631,0.0007408459,0.0007588599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006796093,"about_ca_system_score_gemma":0.0005849835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005596253,"about_ca_topic_score_gemma":0.009092569,"domain_scores_codex":[0.9997012,0.00003245245,0.00001647326,0.00006992695,0.0001090125,0.00007091822],"domain_scores_gemma":[0.9994304,0.0002467542,0.00007609764,0.00005358454,0.000166402,0.00002666859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005774533,0.0003387539,0.004781296,0.0002219656,0.00009073511,0.0002167047,0.00004665822,0.1081869,0.05444585,0.001137655,0.007675116,0.8222808],"study_design_scores_gemma":[0.000006385423,0.00004177716,0.001788325,0.00001179507,0.00001471236,0.00006271986,0.000007970179,0.9829511,0.01396297,0.0004708628,0.0006749911,0.000006393119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2823772,0.004791232,0.6951777,0.0007712161,0.0002582876,0.000117702,0.001518675,0.007254442,0.007733534],"genre_scores_gemma":[0.7945403,0.001086376,0.1948659,0.0001999538,0.00006325281,0.00004790938,0.001391462,0.0001333187,0.007671597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005596253,"threshold_uncertainty_score":0.01112735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114056290499249,"score_gpt":0.2342520444241091,"score_spread":0.2228464153741842,"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."}}