{"id":"W3210191085","doi":"10.1109/tsg.2021.3122099","title":"GNSS Time Signal Spoofing Detector for Electrical Substations","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University Belfast; Queen's University; British Council","keywords":"GNSS applications; Detector; SIGNAL (programming language); Global Positioning System; Spoofing attack; Electronic engineering; Signal processing; Computer science; Electrical engineering; Telecommunications; Engineering; Digital signal processing; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008330588,0.0001400726,0.0001528684,0.0001277365,0.0002319839,0.00003999317,0.000140365,0.00007398712,0.0003665946],"category_scores_gemma":[0.000007075763,0.0001591808,0.0001535116,0.0003990292,0.00001971706,0.0001086339,8.077213e-7,0.0002403848,0.0001786172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008021227,"about_ca_system_score_gemma":0.00005865832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005043014,"about_ca_topic_score_gemma":0.00005468478,"domain_scores_codex":[0.9992191,0.00003176594,0.000226148,0.0001651549,0.0001065997,0.000251201],"domain_scores_gemma":[0.999129,0.000302405,0.00001653461,0.0003596729,0.00009899402,0.00009341267],"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":[0.0001698842,0.001086478,0.00004257408,0.0001285427,0.00064624,0.00003037715,0.0006839415,0.3979569,0.4599822,0.0003921513,0.01958537,0.1192954],"study_design_scores_gemma":[0.001051846,0.0001289003,0.000287867,0.00004281877,0.0001386051,0.00003935909,0.00003021722,0.3989073,0.5208105,0.00015032,0.07788873,0.0005235698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04239414,0.0002624922,0.9547465,0.0002004165,0.0007085349,0.000207291,0.0002103689,0.0003948276,0.0008753924],"genre_scores_gemma":[0.9893187,0.000126493,0.00912724,0.00008446586,0.0001361106,0.0002015697,0.00004668107,0.00005526607,0.0009034304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9469246,"threshold_uncertainty_score":0.6491205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461413245168089,"score_gpt":0.2286532690972367,"score_spread":0.2140391366455558,"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."}}