{"id":"W7125911393","doi":"10.1109/smc58881.2025.11343593","title":"Denoising Near-Infrared Spectroscopy Signal","year":2025,"lang":"","type":"article","venue":"","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Noise reduction; SIGNAL (programming language); Spline (mechanical); Filter (signal processing); Signal averaging; Noise (video); Pattern recognition (psychology); Interpolation (computer graphics)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005042892,0.0005544276,0.0009103743,0.0003274554,0.0004954992,0.0006031494,0.0003487453,0.000345901,0.006498952],"category_scores_gemma":[0.0002588461,0.0005108152,0.0003370298,0.001044497,0.0007041405,0.0002198598,0.0002172368,0.001100804,0.0004526799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004884654,"about_ca_system_score_gemma":0.001015928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001504232,"about_ca_topic_score_gemma":0.000004003122,"domain_scores_codex":[0.9964827,0.0001131582,0.0008490523,0.0008735785,0.0004917309,0.00118976],"domain_scores_gemma":[0.9981313,0.0002505027,0.0001031708,0.0008875463,0.0002404515,0.0003869961],"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":[0.001063617,0.001636225,0.02218672,0.0008861845,0.00084249,0.0004043977,0.0004388721,0.000005182259,0.5480533,0.1105842,0.2982467,0.01565218],"study_design_scores_gemma":[0.001931801,0.001101096,0.003139751,0.001944877,0.0008538972,0.00003188311,0.0002967163,0.01598396,0.9044946,0.02139493,0.04817389,0.0006526104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01925983,0.003664756,0.2360028,0.008475713,0.0007482265,0.0007520484,0.000006533354,0.0009671008,0.730123],"genre_scores_gemma":[0.630473,0.0006052203,0.1851524,0.006721005,0.0004759618,0.00002732954,0.00001429777,0.00007565047,0.1764552],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6112131,"threshold_uncertainty_score":0.9997343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008888613514145586,"score_gpt":0.3242715990813476,"score_spread":0.315382985567202,"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."}}