{"id":"W1998747085","doi":"10.1002/cjs.10050","title":"Unaliasing of aliased line component frequencies","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Aliasing; Undo; Sampling (signal processing); Computer science; Component (thermodynamics); Algorithm; Undoing; Econometrics; Mathematics; Undersampling; Filter (signal processing); Physics; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004819712,0.00007313571,0.000163939,0.0002681379,0.0000600358,0.00008598313,0.0005051431,0.00004834058,0.00004274947],"category_scores_gemma":[0.0002691135,0.0000698603,0.0000357588,0.0001649427,0.0001190153,0.0001908089,0.00001262454,0.0002837985,0.000002496875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003914328,"about_ca_system_score_gemma":0.001270985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00206865,"about_ca_topic_score_gemma":0.01256119,"domain_scores_codex":[0.9991287,0.00004713308,0.000406547,0.00006887182,0.0001990987,0.0001496737],"domain_scores_gemma":[0.9984415,0.0001205329,0.0003527185,0.0002013695,0.0005530448,0.0003308566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004503428,0.000034317,0.001800561,0.00002872178,0.00004107638,0.0002979808,0.003374197,0.0002675572,0.02926234,0.931524,0.01643584,0.01692889],"study_design_scores_gemma":[0.002183569,0.002105899,0.04290009,0.0005481377,0.0001464332,0.002128004,0.0006611797,0.06164453,0.3223202,0.416923,0.1469016,0.001537317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04779727,0.00004804372,0.9503223,0.0008367785,0.000476916,0.00004289243,0.00008457735,0.00001034172,0.0003808952],"genre_scores_gemma":[0.6048207,0.000003628014,0.3949036,0.0002093785,0.00003445133,1.961279e-7,0.000002063385,0.000004112367,0.0000218952],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5570234,"threshold_uncertainty_score":0.7009437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02508655648431516,"score_gpt":0.2520314486694613,"score_spread":0.2269448921851461,"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."}}