{"id":"W2159173415","doi":"10.1109/siecpc.2013.6550768","title":"Time-resolved near-infrared spectroscopic imaging systems","year":2013,"lang":"en","type":"article","venue":"","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Infrared; Near-infrared spectroscopy; Spectroscopy; Functional near-infrared spectroscopy; Computer science; Instrumentation (computer programming); Imaging spectroscopy; Medical imaging; Infrared spectroscopy; Field (mathematics); Materials science; Optics; Artificial intelligence; Physics; Hyperspectral imaging; Neuroscience","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00010863,0.0001792608,0.0003368138,0.00007435367,0.00008892384,0.0002344557,0.0001028719,0.00005408128,0.003314437],"category_scores_gemma":[0.00006425061,0.0001365637,0.00008418823,0.0001482265,0.0001663675,0.0001827342,0.00004369639,0.0002257304,0.003280187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008993891,"about_ca_system_score_gemma":0.00006137848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003511621,"about_ca_topic_score_gemma":2.273643e-7,"domain_scores_codex":[0.9987488,0.00002552178,0.0002699651,0.000287797,0.0002354647,0.0004324769],"domain_scores_gemma":[0.9991282,0.00004182821,0.0000388699,0.0004396637,0.0001227735,0.0002286918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004229824,0.000324336,0.02820848,0.000184844,0.00009999866,0.00009493577,0.0001446677,0.000002060723,0.5446523,0.004444603,0.4191997,0.002601774],"study_design_scores_gemma":[0.005025025,0.001457279,0.04531255,0.001181196,0.0004167565,0.0005819667,0.0007185617,0.572095,0.2760282,0.00531567,0.09003981,0.00182805],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2268085,0.000764953,0.01506586,0.006698443,0.0002772095,0.001597619,0.000002092399,0.002787258,0.7459981],"genre_scores_gemma":[0.8240348,0.00002237711,0.05458465,0.001741478,0.0002676889,0.0001243721,0.00001973979,0.00006428156,0.1191407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6268574,"threshold_uncertainty_score":0.9975967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007349981717224462,"score_gpt":0.2683163651632033,"score_spread":0.2609663834459788,"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."}}