{"id":"W4411643575","doi":"10.3847/1538-3881/add876","title":"A VLBI Software Correlator for Fast Radio Transients","year":2025,"lang":"en","type":"article","venue":"The Astronomical Journal","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto; McGill University","funders":"","keywords":"Very-long-baseline interferometry; Physics; Astronomy; Radio astronomy; Software; Radio telescope; Astrophysics; Remote sensing; Computer science; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002120753,0.0001601633,0.0002295641,0.00008429773,0.0004763357,0.0001024591,0.0004900874,0.00004930119,0.0002065504],"category_scores_gemma":[0.00001041989,0.0001175996,0.0002577108,0.0001230827,0.0001362728,0.0001225067,0.00004372279,0.0004270747,0.00002199905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006257107,"about_ca_system_score_gemma":0.0001588903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001195612,"about_ca_topic_score_gemma":8.631762e-7,"domain_scores_codex":[0.9989938,0.00003777695,0.0003510172,0.0001773885,0.0000643679,0.0003756956],"domain_scores_gemma":[0.9993362,0.0001418281,0.0001283743,0.0002544561,0.00005881852,0.00008035447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002020491,0.0003065043,0.4471224,0.000006933584,0.000777703,3.205157e-7,0.0001939589,0.003742442,0.0002514395,0.1029317,0.01790538,0.4265591],"study_design_scores_gemma":[0.01672794,0.0006980586,0.4276373,0.0002472855,0.00097714,0.00003840591,0.003168038,0.01459889,0.003584978,0.1605986,0.3703777,0.00134556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4444564,0.00003256476,0.5537193,0.0009886253,0.0003479939,0.0001929894,0.00002731662,0.00002171294,0.0002130463],"genre_scores_gemma":[0.9830235,5.184919e-7,0.01558111,0.00004729339,0.0004357964,0.00006210568,0.00001513288,0.00001545504,0.0008190739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5385671,"threshold_uncertainty_score":0.4795572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006294141564365014,"score_gpt":0.2321744579297828,"score_spread":0.2258803163654178,"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."}}