{"id":"W4407389288","doi":"10.1088/1748-0221/20/02/c02020","title":"Experiences and lessons learned from the End-of-Substructure card production of the ATLAS ITk Strip upgrade","year":2025,"lang":"en","type":"article","venue":"Journal of Instrumentation","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"CERN","keywords":"Upgrade; Front and back ends; Detector; Computer science; Atlas (anatomy); Large Hadron Collider; Computer hardware; Substructure; Troubleshooting; Converters; Electrical engineering; Physics; Operating system; Telecommunications; Particle physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01559291,0.0008307833,0.0004707172,0.000812225,0.002801967,0.007126903,0.002606541,0.002263862,0.008556288],"category_scores_gemma":[0.01129661,0.0005695804,0.0004634417,0.0007726494,0.002486418,0.004882613,0.00443546,0.004342382,0.004184582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002769552,"about_ca_system_score_gemma":0.002774462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003857653,"about_ca_topic_score_gemma":0.005153765,"domain_scores_codex":[0.9907346,0.003112069,0.0002698839,0.001030844,0.003283191,0.001569402],"domain_scores_gemma":[0.9917093,0.002006456,0.0003421124,0.0009681386,0.002480618,0.002493351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001384633,0.006161648,0.05330671,0.001012545,0.00009774853,0.01277806,0.103601,0.0109242,0.03241541,0.02159204,0.07531504,0.6814109],"study_design_scores_gemma":[0.0002029099,0.005869595,0.02995461,0.0006829393,0.00006686723,0.006996944,0.09267274,0.007343579,0.04682204,0.009184832,0.7997986,0.0004043069],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7579942,0.004489579,0.03870604,0.03100008,0.001329242,0.0003440512,0.0007558044,0.001333743,0.1640471],"genre_scores_gemma":[0.8682102,0.004191529,0.04424899,0.003896649,0.0006776626,0.00012229,0.001140401,0.001193256,0.07631907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01559291,"threshold_uncertainty_score":0.08246422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02400323627838501,"score_gpt":0.2862270805487982,"score_spread":0.2622238442704132,"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."}}