{"id":"W2087650854","doi":"10.1117/1.jbo.17.5.056002","title":"Changes in diffusion path length with old age in diffuse optical tomography","year":2012,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Université de Montréal; Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Diffuse optical imaging; Context (archaeology); Population; Magnetic resonance imaging; Nuclear magnetic resonance; Optical imaging; Monte Carlo method; Diffusion; Optics; Physics; Medicine; Biology; Tomography; Statistics; Radiology; 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":[0.0007921011,0.0001900656,0.0005953391,0.0007184258,0.00002024589,0.00002356721,0.0001382166,0.0001970396,0.00002463009],"category_scores_gemma":[0.0002729295,0.0001191298,0.0001011673,0.0006504125,0.0003435486,0.0001347235,0.00005283474,0.0009499161,0.000002466106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193641,"about_ca_system_score_gemma":0.00006319879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000835125,"about_ca_topic_score_gemma":0.00001477666,"domain_scores_codex":[0.9979611,0.00004996586,0.0005611871,0.000138419,0.0007431852,0.0005461622],"domain_scores_gemma":[0.9988141,0.0001491552,0.0001496608,0.0001849906,0.00007742216,0.0006246518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001477176,0.02195148,0.8339469,0.0003914278,0.0001648781,0.006008269,0.002041789,6.250565e-7,0.1053388,0.00390744,0.001167546,0.02360364],"study_design_scores_gemma":[0.02552126,0.01740587,0.9067438,0.006359526,0.0005922779,0.00389486,0.001671679,0.001712446,0.02518897,0.001102998,0.008675758,0.001130511],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920687,0.0003237849,0.00195547,0.004457406,0.0001712754,0.0001596596,0.000001701548,0.00003172052,0.0008302896],"genre_scores_gemma":[0.946689,0.0006724916,0.0515037,0.0006313015,0.0004156697,0.000004070112,0.000003847223,0.00002495225,0.0000550065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08014986,"threshold_uncertainty_score":0.4857973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179377885648712,"score_gpt":0.2871309494931998,"score_spread":0.2753371706367127,"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."}}