{"id":"W2513106758","doi":"10.48550/arxiv.1608.06262","title":"ICE: a scalable, low-cost FPGA-based telescope signal processing and networking system","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto; Canadian Institute for Advanced Research; McGill University","funders":"Argonne National Laboratory; Comisión Nacional de Investigación Científica y Tecnológica; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Smithsonian Astrophysical Observatory; U.S. Department of Energy; Office of Science; National Research Council Canada; University of Chicago; National Science Foundation","keywords":"Firmware; Computer hardware; Field-programmable gate array; Motherboard; Computer science; Backplane; Embedded system; Software; Multiplexing; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003872865,0.0006888391,0.0003774819,0.0006003475,0.0002440952,0.0007217358,0.001444587,0.0002843851,0.01167],"category_scores_gemma":[0.0003635898,0.0002576232,0.0002175965,0.0003718127,0.000239295,0.0008633775,0.0005940178,0.0005545079,0.004438426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006717306,"about_ca_system_score_gemma":0.0009427916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343755,"about_ca_topic_score_gemma":0.00220148,"domain_scores_codex":[0.9996407,0.00002416432,0.00001511187,0.00006762357,0.0001748441,0.00007751381],"domain_scores_gemma":[0.9998154,0.00002186947,0.00002129107,0.00003784483,0.00006426882,0.0000392933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001500354,0.0003794611,0.006047374,0.0006458188,0.0001427666,0.0009488523,0.0002392869,0.07694656,0.1688583,0.02290674,0.144768,0.5766165],"study_design_scores_gemma":[0.0005262498,0.001673668,0.00790314,0.0001729637,0.0001415203,0.001430475,0.0001071723,0.4462163,0.151025,0.004048262,0.386558,0.0001972439],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08212993,0.001279586,0.7057247,0.000362612,0.0004503314,0.001082201,0.002888636,0.08894377,0.1171382],"genre_scores_gemma":[0.5231327,0.001124631,0.40195,0.0008226507,0.000216367,0.0008704081,0.01059182,0.001965738,0.05932568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01167,"threshold_uncertainty_score":0.03904003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150350162672073,"score_gpt":0.1750170220264158,"score_spread":0.143513520399695,"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."}}