{"id":"W1984543110","doi":"10.1364/fts.2003.fmd10","title":"Noise consideration in an FTS for telecommunication applications","year":2003,"lang":"en","type":"article","venue":"Fourier Transform Spectroscopy","topic":"Advanced Electrical Measurement Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Noise (video); Computer science; Telecommunications; Noise measurement; Electronic engineering; Acoustics; Engineering; Physics; Noise reduction; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001855056,0.0001462158,0.0001505724,0.0001241828,0.00008396566,0.0000304348,0.0001231538,0.00008543979,0.00001386837],"category_scores_gemma":[0.00002067848,0.0001649394,0.00003981948,0.0002636018,0.00002138437,0.0003319421,0.000001323225,0.0001764609,0.000005224234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002117615,"about_ca_system_score_gemma":0.00002858633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002314182,"about_ca_topic_score_gemma":0.000132724,"domain_scores_codex":[0.9991343,0.00002408575,0.0002659611,0.000171296,0.0001106977,0.0002936323],"domain_scores_gemma":[0.9994986,0.0000517234,0.00001991043,0.0003171874,0.00004711273,0.00006543908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006823496,0.0003622505,0.0009212312,0.000150558,0.00003418452,8.518965e-7,0.0003973998,0.006166859,0.8328224,0.115945,0.0004203414,0.04271067],"study_design_scores_gemma":[0.0006410257,0.0001737309,0.0001626233,0.00001387864,0.00001451193,0.000002345645,0.00002058863,0.009862616,0.8760487,0.09497361,0.01780752,0.0002787871],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007881306,0.0003165981,0.9845645,0.0001886301,0.00003547889,0.001617339,0.000008867673,0.0005420526,0.004845226],"genre_scores_gemma":[0.7774011,0.0001627777,0.2208508,0.00004809261,0.0000268622,0.001423228,0.00003674116,0.00003952879,0.00001083905],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7695199,"threshold_uncertainty_score":0.6726032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740755186157427,"score_gpt":0.2844290745425027,"score_spread":0.2670215226809284,"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."}}