{"id":"W2799712149","doi":"10.1109/access.2018.2832616","title":"Blind System Identification Using Symbolic Dynamics","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cramér–Rao bound; Blind equalization; Computer science; Algorithm; System identification; Binary number; Upper and lower bounds; Kalman filter; Estimation theory; Equalization (audio); Mathematics; Artificial intelligence; Decoding methods; Data modeling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003153359,0.0004136123,0.0005401681,0.0006440725,0.00044055,0.0005586143,0.0003527507,0.0004976023,0.001398067],"category_scores_gemma":[0.0014119,0.00020423,0.0004243835,0.0003304302,0.0007093732,0.001030036,0.0009556181,0.0006192355,0.0004017793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004537044,"about_ca_system_score_gemma":0.0007090032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001339159,"about_ca_topic_score_gemma":0.0009163063,"domain_scores_codex":[0.9997339,0.00008050971,0.00001425984,0.00004618515,0.0001094903,0.00001562146],"domain_scores_gemma":[0.9996722,0.0001730568,0.00004572674,0.00004159677,0.00005450008,0.00001292211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001340154,0.00003316173,0.001046998,0.000310432,0.0001088967,0.0002136405,0.000351093,0.5217293,0.03027809,0.2790984,0.001125085,0.1655709],"study_design_scores_gemma":[0.000006448055,0.00002149214,0.0001015212,0.00001072963,0.00000633703,0.00005233747,0.00001078821,0.9734822,0.002161867,0.02239757,0.001736607,0.00001202543],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004279818,0.0001902068,0.9939837,0.00007375213,0.00002472673,0.00001109486,0.00001128698,0.0001890101,0.001236539],"genre_scores_gemma":[0.6123134,0.0009133666,0.3795688,0.00009919489,0.00007342575,0.000155634,0.0001153245,0.0001040648,0.00665677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001398067,"threshold_uncertainty_score":0.004676938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05870219725668323,"score_gpt":0.3629815099072791,"score_spread":0.3042793126505958,"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."}}