{"id":"W2158727422","doi":"10.1016/j.media.2007.06.002","title":"Activation detection in diffuse optical imaging by means of the general linear model","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université de Montréal; Polytechnique Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Canada Research Chairs; Institut National de la Santé et de la Recherche Médicale","keywords":"Computer science; General linear model; Functional magnetic resonance imaging; Artificial intelligence; Diffuse optical imaging; Communication noise; Computer vision; Noise (video); Functional imaging; Basis (linear algebra); Pattern recognition (psychology); Linear model; Image (mathematics); Iterative reconstruction; Machine learning; Mathematics; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006485043,0.0006157139,0.0007038339,0.0002929533,0.0002150827,0.0005859947,0.0007051686,0.0008228022,0.0008332137],"category_scores_gemma":[0.001668403,0.0004157275,0.0006208234,0.0004034383,0.0006675565,0.0009175277,0.0007038145,0.0009903645,0.000394033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003593861,"about_ca_system_score_gemma":0.0005464306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002166922,"about_ca_topic_score_gemma":0.002717122,"domain_scores_codex":[0.9997403,0.0001192187,0.0000081036,0.00005816858,0.00005001287,0.00002419846],"domain_scores_gemma":[0.9996152,0.000246728,0.00003524614,0.00002904372,0.00005471841,0.00001906898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003972692,0.00008017704,0.0009630924,0.0003072342,0.0001493508,0.0001651793,0.0001800271,0.7335656,0.05244248,0.05043222,0.002048708,0.1592686],"study_design_scores_gemma":[0.000007431343,0.00001937219,0.000154605,0.000003297887,0.000009662121,0.00003222323,0.000004532165,0.9906722,0.002144228,0.006655416,0.0002867509,0.00001027714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01021895,0.0001677127,0.9889685,0.0001227498,0.00001016412,0.000008079929,0.00002192767,0.0001421833,0.0003397709],"genre_scores_gemma":[0.6053022,0.001062378,0.3849891,0.0002086434,0.00009911131,0.0001630876,0.0002260321,0.0001970908,0.007752397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002166922,"threshold_uncertainty_score":0.004308641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007082030066336433,"score_gpt":0.3184582171745209,"score_spread":0.3113761871081845,"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."}}