{"id":"W4221103586","doi":"10.1088/1748-0221/17/03/p03026","title":"Automated visual inspection and defect detection of large-scale silicon strip sensors","year":2022,"lang":"en","type":"article","venue":"Journal of Instrumentation","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; TRIUMF","funders":"","keywords":"Detector; Computer science; Automated X-ray inspection; Visual inspection; Image sensor; Pixel; Process (computing); Reliability (semiconductor); Artificial intelligence; Computer vision; Upgrade; Tracking (education); Computer hardware; Image processing; Image (mathematics); Physics","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":[0.0001769167,0.00005243631,0.00009098763,0.0001553583,0.0001136004,0.00001579337,0.00003345836,0.00002197976,0.000009304214],"category_scores_gemma":[0.000006901632,0.0000559599,0.00003799769,0.0002037272,0.00001231878,0.0001501856,0.00001333581,0.0001296522,2.709249e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073643,"about_ca_system_score_gemma":0.00001137051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005233671,"about_ca_topic_score_gemma":0.000003031884,"domain_scores_codex":[0.9994746,0.00002453267,0.0002574928,0.00004646703,0.0001345959,0.00006225997],"domain_scores_gemma":[0.9997076,0.00001043156,0.0001734538,0.00003410361,0.00005367785,0.00002067739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004336386,0.00007748503,0.001249165,0.00007350785,0.00004021571,0.000001323683,0.0008093033,0.01144455,0.9494452,0.00002743176,0.00015352,0.03663492],"study_design_scores_gemma":[0.0006458681,0.0003446375,0.009786334,0.00002197946,0.00004571418,0.0001627906,0.001481526,0.5064474,0.4805038,0.000206091,0.0002636002,0.00009031093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793513,0.00006013204,0.02013862,0.00002448334,0.0001068717,0.0000710012,0.00000559062,0.0001810163,0.00006094992],"genre_scores_gemma":[0.9981967,0.00003884888,0.001702706,0.00001001326,0.00002672539,0.000008053168,0.000004404711,0.00001012056,0.000002389281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4950028,"threshold_uncertainty_score":0.2281979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005832251691774735,"score_gpt":0.262065329510939,"score_spread":0.2562330778191642,"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."}}