{"id":"W2529635858","doi":"10.1364/boe.7.004514","title":"Subtraction-based approach for enhancing the depth sensitivity of time-resolved NIRS","year":2016,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Lawson Health Research Institute","funders":"National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; National Institutes of Health; Narodowe Centrum Nauki; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Subtraction; Sensitivity (control systems); Computer science; Background subtraction; Optics; Computer vision; Artificial intelligence; Physics; Mathematics","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":[0.0009732033,0.0001514333,0.0003423334,0.00007911655,0.00008346693,0.00001423799,0.0001083384,0.0001501304,0.00002532235],"category_scores_gemma":[0.0008739963,0.00007785071,0.0001590236,0.0001317934,0.0005776085,0.00004942934,0.00002642229,0.0001521341,0.000005684344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005386115,"about_ca_system_score_gemma":0.0001553569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002509292,"about_ca_topic_score_gemma":0.000002186776,"domain_scores_codex":[0.9985798,0.00008199335,0.0003451171,0.0002694767,0.000403436,0.000320152],"domain_scores_gemma":[0.9977698,0.00136029,0.0001031724,0.0003980506,0.0001741002,0.0001945259],"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":[0.000215407,0.0004402972,0.0001602835,0.0001189897,0.00004425075,0.000005709285,0.00002984323,0.000002338055,0.9930305,0.0005094548,0.001793868,0.003649083],"study_design_scores_gemma":[0.00146982,0.0004849014,0.0002796492,0.000278401,0.0001536291,0.00001888585,0.00004030888,0.04053693,0.9515094,0.0001550932,0.00489531,0.0001776828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01983452,0.00003393466,0.9747842,0.002823788,0.00007801082,0.0005174199,0.00003572557,0.0001544225,0.001738],"genre_scores_gemma":[0.8361808,0.00001694945,0.1627667,0.0002086549,0.0002156089,0.00005110649,0.00002822128,0.00002728867,0.0005046914],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8163463,"threshold_uncertainty_score":0.317466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01738811930478398,"score_gpt":0.2961457807425534,"score_spread":0.2787576614377694,"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."}}