{"id":"W2009112874","doi":"10.1364/boe.4.002032","title":"Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images","year":2013,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Ontario Ministry of Economic Development and Innovation","keywords":"Optical coherence tomography; Optics; Computer science; Coherence (philosophical gambling strategy); Conditional random field; Tomography; Iterative reconstruction; Diffuse optical imaging; Random field; Artificial intelligence; Computer vision; Physics; Mathematics; Statistics","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":[0.0001652201,0.0002484854,0.0003775521,0.0002715323,0.00007090053,0.00006735467,0.0004409748,0.0002541935,0.000403384],"category_scores_gemma":[0.0001791588,0.0002295617,0.0001684252,0.0006635556,0.0005046471,0.0002085987,0.00008137001,0.0003271937,0.00008047585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002242071,"about_ca_system_score_gemma":0.00002783195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000288048,"about_ca_topic_score_gemma":9.417804e-7,"domain_scores_codex":[0.9981323,0.00003579329,0.0005838501,0.000358584,0.000465946,0.0004234902],"domain_scores_gemma":[0.9983875,0.0004071567,0.00004776108,0.000414028,0.0002216992,0.0005218837],"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":[0.0001166757,0.001718773,0.001663968,0.0005311465,0.0004121227,0.000005076223,0.000367089,0.001981581,0.9347067,0.00814273,0.0188955,0.03145859],"study_design_scores_gemma":[0.01730591,0.001789071,0.06195851,0.0008973352,0.0005505573,0.0002159746,0.002020109,0.4409124,0.4496133,0.01126813,0.008613357,0.004855325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1340192,0.0001251963,0.8552998,0.0003489366,0.0003013722,0.001314605,0.0001799138,0.0003538917,0.008057102],"genre_scores_gemma":[0.7207081,0.0000150963,0.278468,0.00005713757,0.00008458792,0.0005488353,0.00007109615,0.00002272846,0.00002440066],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5866889,"threshold_uncertainty_score":0.9361256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474941066638766,"score_gpt":0.2369908412865561,"score_spread":0.2222414306201685,"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."}}