{"id":"W2548946164","doi":"10.1364/boe.7.004899","title":"Multi-modal automatic montaging of adaptive optics retinal images","year":2016,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Eye Institute; National Institutes of Health; Foundation Fighting Blindness; Paul MacKall and Evanina Bell MacKall Trust; Research to Prevent Blindness; F. M. Kirby Foundation","keywords":"Computer science; Artificial intelligence; Classification of discontinuities; Computer vision; Discontinuity (linguistics); Adaptive optics; Pattern recognition (psychology); Image processing; Algorithm; Image (mathematics); Optics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002854813,0.0001859789,0.0002800913,0.0001520508,0.00007794573,0.00004869975,0.0009838978,0.00007680131,0.00002262907],"category_scores_gemma":[0.000308492,0.0001245569,0.00008666007,0.0002570834,0.0004106002,0.0005069621,0.000592047,0.0001322204,0.00003019499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004344501,"about_ca_system_score_gemma":0.00007616598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004254761,"about_ca_topic_score_gemma":1.795199e-7,"domain_scores_codex":[0.9980706,0.00006438819,0.0004554914,0.000402229,0.0006034417,0.0004038373],"domain_scores_gemma":[0.9984998,0.000306908,0.0001950458,0.000571753,0.0001522571,0.0002742466],"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":[0.00002751876,0.000595503,0.000296904,0.0000804369,0.00005654421,0.000143014,0.0009300492,0.00002351786,0.5408176,0.01785108,0.001023095,0.4381548],"study_design_scores_gemma":[0.001837783,0.0002665294,0.001569142,0.0006565672,0.00001587164,0.00003594814,0.0001406124,0.9469311,0.04507939,0.001899639,0.001151075,0.0004163915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004529294,0.00008416281,0.9932722,0.001084981,0.0003613813,0.0001177837,0.00001161338,0.0001706132,0.0003679959],"genre_scores_gemma":[0.3293474,0.00003325455,0.6702936,0.00007708942,0.00005648199,0.000006624966,8.84623e-7,0.0000116536,0.0001730013],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9469075,"threshold_uncertainty_score":0.5079284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207288376427705,"score_gpt":0.2890868313553638,"score_spread":0.2683579937125933,"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."}}