{"id":"W4386188647","doi":"10.2139/ssrn.4548759","title":"Defect Detection in Pharmaceutical Vials Using Computer Vision System","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Vial; Computer vision; Computer science; Artificial intelligence; Process engineering; Chromatography; Chemistry; Engineering","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01545545,0.0004303217,0.0008402626,0.001019757,0.0003668605,0.000621155,0.001162134,0.00034808,0.00001018087],"category_scores_gemma":[0.001866551,0.0003606791,0.0003766599,0.0009358021,0.00009235425,0.0003815957,0.0009126895,0.007685807,0.0001985711],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005579198,"about_ca_system_score_gemma":0.001803012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007924739,"about_ca_topic_score_gemma":0.0003665365,"domain_scores_codex":[0.991054,0.0009857918,0.001735656,0.001108518,0.002247732,0.002868317],"domain_scores_gemma":[0.9961662,0.001825801,0.0008460513,0.0004968586,0.0004355325,0.0002295192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005425031,0.0001334371,0.001870376,0.0001851954,0.0002793019,0.0003596708,0.0002626959,0.2562639,0.003469054,0.01034915,0.0000708308,0.7262139],"study_design_scores_gemma":[0.0007556778,0.000235083,0.0005579957,0.00058798,0.00007542958,0.000843951,0.0009705288,0.4546596,0.0003803435,0.5401304,0.0002794948,0.0005234636],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.146248,0.001029185,0.8476751,0.0001490294,0.004421624,0.0003046636,0.000008716162,0.0001261843,0.00003748548],"genre_scores_gemma":[0.9942998,0.0004313981,0.003734804,0.00003302817,0.00131044,0.00001277185,0.000002056264,0.00007619189,0.00009951272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8480518,"threshold_uncertainty_score":0.9998845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1598160647322673,"score_gpt":0.478734726355055,"score_spread":0.3189186616227878,"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."}}