{"id":"W4210450135","doi":"10.1109/taes.2022.3145296","title":"A Likelihood Ratio Detector for QTMS Radar and Noise Radar","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Radar; Receiver operating characteristic; Lemma (botany); Algorithm; Mathematics; Function (biology); Physics; Computer science; Statistics; Optics; 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":[0.0003612821,0.0001819879,0.0002105324,0.000196133,0.0008593925,0.0002232247,0.0002419858,0.00005076978,0.00001092935],"category_scores_gemma":[0.000001628994,0.0001794386,0.00009684863,0.0004209234,0.00003519302,0.0003113111,0.000004596133,0.0002665896,0.000005191041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009862002,"about_ca_system_score_gemma":0.0001358839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007109998,"about_ca_topic_score_gemma":0.0000513565,"domain_scores_codex":[0.9986076,0.00007948599,0.0002529169,0.0003255544,0.0002728856,0.000461577],"domain_scores_gemma":[0.9993428,0.00008645862,0.0001004786,0.0003031098,0.00004232552,0.0001248284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001845934,0.002070432,0.0001615366,0.001600278,0.001804183,0.0000228533,0.0283312,0.02167384,0.1338735,0.4922793,0.01602373,0.3003132],"study_design_scores_gemma":[0.01290918,0.00963266,0.0001721136,0.0001237337,0.0002369268,0.0009893019,0.007779728,0.7164183,0.04086597,0.003101756,0.205211,0.002559315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03833464,0.001134746,0.9582824,0.0006570381,0.000628487,0.0006976406,0.00003200821,0.0001535578,0.0000795138],"genre_scores_gemma":[0.9980415,0.0002075068,0.0006857919,0.0002825598,0.00003124531,0.0004885255,0.000002076183,0.0000150784,0.0002456761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9597069,"threshold_uncertainty_score":0.7317294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007015246153559748,"score_gpt":0.206715141761982,"score_spread":0.1996998956084223,"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."}}