{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003999812,0.0004675599,0.0004402009,0.001012596,0.0001448569,0.0004497967,0.0007962078,0.0005660523,0.001494461],"category_scores_gemma":[0.0009266351,0.0004275485,0.0002574364,0.0003875821,0.0002711691,0.0003449569,0.0005075258,0.0003598898,0.000567207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000244825,"about_ca_system_score_gemma":0.0002508422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004704826,"about_ca_topic_score_gemma":0.000879623,"domain_scores_codex":[0.9994469,0.00006047272,0.00002428966,0.0001654473,0.0002474324,0.00005533815],"domain_scores_gemma":[0.9990791,0.0002985451,0.0001300063,0.0001766166,0.0002573025,0.00005845186],"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":[0.0001150916,0.00004884479,0.001732975,0.000124395,0.00001245404,0.00008388525,0.00007925132,0.001559656,0.9522704,0.0001400608,0.0004172382,0.04341575],"study_design_scores_gemma":[0.00002620305,0.0003993897,0.02766253,0.00002181801,0.00002145371,0.0005359622,0.00009484691,0.05649081,0.9120453,0.0002455948,0.002414857,0.00004125917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4927063,0.0005030414,0.4981123,0.00007167878,0.00006126248,0.0001846674,0.0004780708,0.005772483,0.002110137],"genre_scores_gemma":[0.6793551,0.0002325308,0.3170548,0.00005198622,0.00001894961,0.0001536674,0.0005409267,0.000281011,0.002311008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001494461,"threshold_uncertainty_score":0.004999518,"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."}}