{"id":"W2741519841","doi":"10.1142/s2251171717500076","title":"Bifrost: A Python/C++ Framework for High-Throughput Stream Processing in Astronomy","year":2017,"lang":"en","type":"article","venue":"Journal of Astronomical Instrumentation","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Air Force Office of Scientific Research; Air Force Research Laboratory; Office of Naval Research; McGill University; National Science Foundation","keywords":"Python (programming language); Computer science; Software; Pipeline (software); Data processing; Radio astronomy; Throughput; Pipeline transport; Stream processing; Beamforming; Real-time computing; Computer hardware; Operating system; Astronomy; Engineering; Physics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002526593,0.0001913005,0.0003835867,0.0001862942,0.0002781642,0.0002608584,0.0004491645,0.00008684518,0.00009658848],"category_scores_gemma":[0.00003072714,0.0001886173,0.0001523198,0.00006427862,0.0001410746,0.001139329,0.00006229417,0.0003531541,0.000004838388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520099,"about_ca_system_score_gemma":0.000192984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001026002,"about_ca_topic_score_gemma":0.000008195168,"domain_scores_codex":[0.9985476,0.00002514462,0.0007613498,0.0002232817,0.0001145148,0.0003281207],"domain_scores_gemma":[0.9981301,0.00005324046,0.001327325,0.000291711,0.000110186,0.00008742807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001300483,0.0001963925,0.4888861,0.000008545591,0.00005298416,4.201291e-7,0.00008066575,0.0005321677,0.0002630322,0.04267323,0.00005757836,0.4671188],"study_design_scores_gemma":[0.007056312,0.001044728,0.8875989,0.0003233142,0.0001355932,0.000004340787,0.002603525,0.001881285,0.009440074,0.08589486,0.003512737,0.0005043534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6697111,0.000009059013,0.3290048,0.0007120157,0.0002476651,0.000201439,0.00002236899,0.000006747755,0.00008479279],"genre_scores_gemma":[0.7091911,5.256225e-7,0.2902356,0.000008154851,0.000479032,0.00003217942,0.00002112634,0.00001756317,0.00001470001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4666145,"threshold_uncertainty_score":0.7691592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02076411710274455,"score_gpt":0.305572932648131,"score_spread":0.2848088155453865,"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."}}