{"id":"W4200499474","doi":"10.1029/2021rs007376","title":"Automated Detection of Antenna Malfunctions in Large‐ <i>N</i> Interferometers: A Case Study With the Hydrogen Epoch of Reionization Array","year":2021,"lang":"en","type":"article","venue":"Radio Science","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"FP7 International Cooperation; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Research Foundation; Massachusetts Institute of Technology; National Science Foundation; American Academy of Periodontology Foundation; McGill University; Gordon and Betty Moore Foundation","keywords":"Reionization; Epoch (astronomy); Astronomical interferometer; Antenna (radio); Antenna array; Astronomy; Physics; Computer science; Interferometry; Remote sensing; Telecommunications; Redshift; Stars; Geology; Galaxy","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":[0.001464982,0.0004939693,0.0003616761,0.001997135,0.0004576013,0.0007255248,0.0008339436,0.0005582888,0.0005294],"category_scores_gemma":[0.006714557,0.0001605859,0.0002742713,0.001901908,0.0004624193,0.0005947239,0.0007698261,0.0004731313,0.000295727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004595888,"about_ca_system_score_gemma":0.0003680887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007732018,"about_ca_topic_score_gemma":0.01152793,"domain_scores_codex":[0.9991444,0.0002051556,0.00009071546,0.0001515424,0.0003258721,0.00008227112],"domain_scores_gemma":[0.9923138,0.003475736,0.001493261,0.001228867,0.001221797,0.0002665037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003027472,0.0001622009,0.7359249,0.0001174506,0.0001208245,0.001783518,0.001220603,0.0845007,0.02499924,0.001520554,0.005725133,0.1436221],"study_design_scores_gemma":[0.00004611243,0.0002483712,0.4726256,0.00004812163,0.00006117761,0.001266474,0.00141378,0.4828705,0.03072201,0.003318359,0.007294172,0.00008543544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301693,0.000149935,0.06368107,0.0003022267,0.00002367153,0.0001029225,0.001524727,0.002378996,0.001667107],"genre_scores_gemma":[0.9519436,0.00003076792,0.04678676,0.00003145169,0.00001133289,0.00002851723,0.0008370678,0.00008287358,0.0002476138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007732018,"threshold_uncertainty_score":0.015374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00891388833826666,"score_gpt":0.2341104058978857,"score_spread":0.2251965175596191,"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."}}