{"id":"W4396919429","doi":"10.1016/j.nima.2024.169446","title":"Identification and recovery of ATLAS18 strip sensors with high surface static charge","year":2024,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Carleton University","funders":"Agencia Estatal de Investigación; Natural Sciences and Engineering Research Council of Canada; Science and Technology Facilities Council; Forskningsrådet om Hälsa, Arbetsliv och Välfärd; CERN; U.S. Department of Energy","keywords":"Identification (biology); Charge (physics); Surface charge; Surface (topology); Materials science; Computer science; Acoustics; Physics; Geometry; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00133986,0.0002150411,0.0003133336,0.0002697719,0.0002312126,0.0003178741,0.0000797664,0.00006868404,0.00008231081],"category_scores_gemma":[0.00001783032,0.000187358,0.00003801825,0.000871798,0.0001565581,0.0004525403,0.00009915348,0.0004552726,0.000002381797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835945,"about_ca_system_score_gemma":0.00005421861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003016816,"about_ca_topic_score_gemma":0.000006559788,"domain_scores_codex":[0.9979927,0.000340777,0.0003763735,0.0004573476,0.0003889188,0.0004438877],"domain_scores_gemma":[0.9993489,0.0001880361,0.0001239781,0.0001302622,0.00008043861,0.0001283659],"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":[0.0002640877,0.0003195283,0.05838878,0.0002900035,0.0007559751,0.000004929921,0.003576398,0.00005239265,0.6907153,0.001779675,0.00006471108,0.2437882],"study_design_scores_gemma":[0.003993854,0.002381346,0.1191833,0.0009436499,0.0001469449,0.000006556569,0.003884066,0.03924233,0.8158916,0.01215761,0.0009343714,0.001234378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987374,0.00006173828,0.000333742,0.00004360126,0.0002047823,0.0003618992,0.00003465494,0.00003370807,0.0001884835],"genre_scores_gemma":[0.9970267,0.0003027403,0.002442936,0.000006390105,0.00005969909,0.0000204969,0.0000119389,0.00003425535,0.00009479285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2425538,"threshold_uncertainty_score":0.7640239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471758534197652,"score_gpt":0.3482733255341821,"score_spread":0.3135557401922056,"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."}}