{"id":"W2004995977","doi":"10.1088/0967-3334/33/2/259","title":"Wavelet-based motion artifact removal for functional near-infrared spectroscopy","year":2012,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":614,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; James S. McDonnell Foundation","keywords":"Artifact (error); Wavelet; Functional near-infrared spectroscopy; Distortion (music); Attenuation; Artificial intelligence; SIGNAL (programming language); Computer science; Sensitivity (control systems); Energy (signal processing); Computer vision; Mean squared error; Pattern recognition (psychology); Mathematics; Physics; Optics; Statistics; Engineering; Electronic engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006058028,0.0002093795,0.0003358213,0.00003947771,0.0001556772,0.00002853042,0.00006117845,0.0001036104,0.0002796631],"category_scores_gemma":[0.0003903344,0.0001483014,0.000225888,0.0001066675,0.0001182442,0.00008082222,0.00001894774,0.0002218525,0.00007252561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003222495,"about_ca_system_score_gemma":0.00006849519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003570819,"about_ca_topic_score_gemma":1.324936e-7,"domain_scores_codex":[0.9982576,0.00005755778,0.0002680497,0.0003098897,0.0005507311,0.0005561279],"domain_scores_gemma":[0.9991213,0.00005018598,0.00006664293,0.0002678583,0.0002400222,0.000253957],"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":[0.0007385503,0.001109669,0.001089091,0.00006477804,0.00004783917,0.000002285857,0.00001112503,0.000005001697,0.9825879,0.001505596,0.009748277,0.003089851],"study_design_scores_gemma":[0.002048457,0.00198751,0.2289544,0.0001086057,0.0001726174,0.0000226901,0.00001475551,0.005417972,0.7461602,0.004650175,0.01007214,0.0003904553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6050702,0.000466631,0.3784068,0.003519578,0.0006972288,0.002027868,0.00001583788,0.001052361,0.00874351],"genre_scores_gemma":[0.8980281,0.000004336565,0.1001427,0.0009730762,0.0005307969,0.0001541439,0.00005023536,0.00001924414,0.00009729226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2929579,"threshold_uncertainty_score":0.6047555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245193182121399,"score_gpt":0.329330899170997,"score_spread":0.2048115809588571,"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."}}