{"id":"W2791729216","doi":"10.1109/tim.2018.2800258","title":"Automated Machine Vision System for Liquid Particle Inspection of Pharmaceutical Injection","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer vision; Artificial intelligence; Computer science; Machine vision; Visual inspection; Process (computing); Automated X-ray inspection; Image segmentation; Segmentation; Image processing; Image (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":[0.0001820437,0.00008985001,0.0000857117,0.00007867854,0.0001875934,0.00002161157,0.00003092509,0.0000418842,0.000009075002],"category_scores_gemma":[0.000001517933,0.00009044932,0.00003089811,0.0001542069,0.00005042282,0.0001235598,4.555897e-7,0.00005565563,0.000003916923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514494,"about_ca_system_score_gemma":0.0000120735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001296975,"about_ca_topic_score_gemma":0.00001545374,"domain_scores_codex":[0.9993677,0.00001491822,0.0002253174,0.0001218177,0.0001678596,0.0001023649],"domain_scores_gemma":[0.9996774,0.00000779607,0.00003717415,0.00008054898,0.0001514818,0.00004556387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003421032,0.0002094285,0.000009385867,0.0003483848,0.00006908984,9.041139e-8,0.0004941267,0.003511061,0.876152,0.0003066884,0.0002436418,0.118314],"study_design_scores_gemma":[0.0004879436,0.000382382,0.00003168485,0.00005769785,0.00003435231,0.000004360682,0.00009784182,0.3186716,0.6798984,0.000009355918,0.0002627099,0.00006173013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1403857,0.00002728691,0.8575122,0.00005793277,0.0003159913,0.0003753288,0.00001712396,0.001127413,0.0001810085],"genre_scores_gemma":[0.9962268,0.00002691063,0.003484728,0.00002218397,0.00002701218,0.0001872562,0.000003068013,0.00001600063,0.000006036519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.855841,"threshold_uncertainty_score":0.3688416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410147606779279,"score_gpt":0.3099858668567765,"score_spread":0.2758843907889837,"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."}}