{"id":"W2978725510","doi":"","title":"UMP invariance in adaptive detection: kernels that preserve monotone likelihood ratio","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Workshop on Statistical Signal Processing","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Mathematics; Monotone polygon; Invariant (physics); Statistic; Estimator; Sufficient statistic; Test statistic; Applied mathematics; Noise power; Likelihood-ratio test; Statistics; Statistical hypothesis testing; Power (physics)","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001256431,0.001050754,0.001101761,0.0004744357,0.0007562111,0.00108514,0.0005458295,0.0005618716,0.0002607391],"category_scores_gemma":[0.0001485668,0.001032992,0.0001477488,0.001610779,0.0003135373,0.001599899,0.00004237473,0.001800917,0.00008843186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005427299,"about_ca_system_score_gemma":0.000473075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003553131,"about_ca_topic_score_gemma":0.00007367716,"domain_scores_codex":[0.9938573,0.0003882307,0.00142904,0.00131356,0.001351971,0.001659905],"domain_scores_gemma":[0.9976518,0.0007405263,0.0003769874,0.0003388985,0.000316372,0.0005753986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005494208,0.0004637568,0.0002317755,0.001643183,0.0001016081,0.0004257725,0.001452903,0.1734233,0.014127,0.0008526811,0.0004138444,0.8063147],"study_design_scores_gemma":[0.001933266,0.0003275804,0.000296768,0.003839335,0.00009287253,0.0001196252,0.001194915,0.9392566,0.03762632,0.01271596,0.0006826519,0.00191412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01017081,0.004437293,0.9753237,0.00004708675,0.0003260168,0.0007407111,0.0000360751,0.000634907,0.008283357],"genre_scores_gemma":[0.9918008,0.00003836578,0.006880424,0.0001879421,0.00041903,0.0002269727,0.00001344467,0.0002503733,0.0001826518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.98163,"threshold_uncertainty_score":0.9999518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03118632328080612,"score_gpt":0.2591838037555631,"score_spread":0.227997480474757,"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."}}