{"id":"W4285585921","doi":"10.1117/12.2630286","title":"RF-ICE: large-scale gigahertz readout of frequency-multiplexed microwave kinetic inductance detectors","year":2022,"lang":"en","type":"article","venue":"Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XI","topic":"Superconducting and THz Device Technology","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Three-Speed Logic (Canada); Canadian Institute for Advanced Research; McGill University","funders":"Fermilab; Natural Sciences and Engineering Research Council of Canada; McGill University; Canadian Institute for Advanced Research; U.S. Department of Energy","keywords":"Detector; Microwave; Multiplexing; Physics; Bandwidth (computing); Radio frequency; Optoelectronics; Electronic engineering; Electrical engineering; Computer science; Optics; Engineering; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003512487,0.0005213526,0.0006911044,0.000431849,0.0006941454,0.00008868999,0.0003257962,0.0001341576,0.0003413901],"category_scores_gemma":[0.00001709461,0.0005473142,0.000260186,0.00043237,0.0002800007,0.0003700343,0.0002441221,0.0004196037,0.000003306858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009219734,"about_ca_system_score_gemma":0.000074854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000576648,"about_ca_topic_score_gemma":0.00003503712,"domain_scores_codex":[0.9971819,0.0001134436,0.0008634173,0.0008424978,0.0002700092,0.0007287438],"domain_scores_gemma":[0.9985887,0.0001722839,0.0004603242,0.000463564,0.0001190264,0.0001961465],"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":[0.0001505817,0.0003088891,0.3209734,0.0001072945,0.0005218888,0.000001428346,0.002836744,0.000009713161,0.498713,0.0007852795,0.00008810678,0.1755037],"study_design_scores_gemma":[0.0180695,0.004948632,0.04616833,0.000133385,0.0009211133,0.00006504569,0.09283089,0.001006321,0.7922302,0.01248992,0.02811535,0.003021233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877483,0.0008312422,0.008679455,0.00003828738,0.0005525513,0.0009191994,0.0008982585,0.00009494606,0.0002378143],"genre_scores_gemma":[0.9729455,0.00003032876,0.02578418,0.00007582911,0.0001158133,0.0004637281,0.000375526,0.00007320005,0.0001359163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2935173,"threshold_uncertainty_score":0.9996979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228199243453851,"score_gpt":0.2320944008109916,"score_spread":0.2198124083764531,"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."}}