{"id":"W2898579564","doi":"10.1117/1.nph.5.4.040401","title":"Effect of prewhitening in resting-state functional near-infrared spectroscopy data","year":2018,"lang":"en","type":"article","venue":"Neurophotonics","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministerio de Economía y Competitividad; Eusko Jaurlaritza","keywords":"Preprocessor; Spurious relationship; Autocorrelation; Computer science; SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Statistics; Mathematics; Machine learning","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":[0.0005113384,0.0001598511,0.0003553948,0.0001024159,0.00005608644,0.00003070098,0.0002111794,0.00005532215,0.00008058528],"category_scores_gemma":[0.0008419334,0.0001363684,0.0000473155,0.000356877,0.0002751714,0.0000978116,0.0001834942,0.0003995905,0.00002527305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003911738,"about_ca_system_score_gemma":0.000129246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000377007,"about_ca_topic_score_gemma":0.000004535556,"domain_scores_codex":[0.9985499,0.00009979802,0.0003130677,0.0004459612,0.0002851727,0.0003060904],"domain_scores_gemma":[0.9986027,0.0002118145,0.00008503936,0.0009225989,0.00008566576,0.0000921383],"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":[0.002842068,0.0002205122,0.05838592,0.0002874995,0.00003465372,0.0002394415,0.0001328408,0.00001367551,0.9300557,0.00007716629,0.005814742,0.001895821],"study_design_scores_gemma":[0.001726708,0.005823935,0.01743916,0.0002986188,0.00008202135,0.0001144377,0.000003014414,0.06888666,0.9005448,0.0002011223,0.004696836,0.0001826333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984884,0.00007641494,0.0006559403,0.0001391328,0.0001966614,0.0003569782,0.00001573491,0.0001809681,0.01349416],"genre_scores_gemma":[0.9647059,0.000067542,0.03378476,0.0003582625,0.0001244452,0.00001295622,0.00005038988,0.00005710772,0.0008386548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06887298,"threshold_uncertainty_score":0.556094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283312720455686,"score_gpt":0.3338572241699153,"score_spread":0.3110240969653584,"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."}}