{"id":"W2917749114","doi":"10.1109/isspit.2018.8642749","title":"Signal Identification Based On Internal Model in Discrete Time","year":2018,"lang":"en","type":"article","venue":"","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Internal model; Computer science; Harmonics; SIGNAL (programming language); Identification (biology); Noise (video); Function (biology); Algorithm; Filter (signal processing); Control theory (sociology); Tracking (education); Artificial intelligence; Engineering; Computer vision","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":[0.0004737461,0.0004838339,0.0006232751,0.0004896928,0.0003112443,0.0008642101,0.0006525129,0.0006387633,0.00106716],"category_scores_gemma":[0.001392398,0.0002583313,0.0006410839,0.000378565,0.000668927,0.001079445,0.0005674412,0.001008556,0.0004981179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00032745,"about_ca_system_score_gemma":0.0003458238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008284987,"about_ca_topic_score_gemma":0.0005462614,"domain_scores_codex":[0.9994828,0.00008897503,0.00002413954,0.0001130027,0.0002613197,0.0000297611],"domain_scores_gemma":[0.9995124,0.0002146456,0.00006462253,0.00009594091,0.00009529157,0.00001707122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001271991,0.00006665573,0.001332736,0.0002254304,0.00008064729,0.0001305053,0.0002581508,0.5991267,0.04342779,0.06059226,0.001018406,0.2936135],"study_design_scores_gemma":[0.000003635608,0.00003188611,0.0001095074,0.000006826907,0.000006730623,0.00004941072,0.00000555336,0.9917291,0.003666655,0.003468683,0.0009143155,0.000007761912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002363683,0.00003971909,0.9969908,0.00001558752,0.00001169173,0.000004716405,0.000004042937,0.0001581142,0.0004116599],"genre_scores_gemma":[0.4916371,0.0003625785,0.5040983,0.00008031433,0.00007219344,0.0001097544,0.0001594433,0.0001480201,0.003332264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00106716,"threshold_uncertainty_score":0.00357002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508457220088427,"score_gpt":0.2885374393756858,"score_spread":0.2734528671748015,"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."}}