{"id":"W2145558866","doi":"10.1364/boe.5.000547","title":"Wavefront sensorless adaptive optics optical coherence tomography for in vivo retinal imaging in mice","year":2014,"lang":"en","type":"article","venue":"Biomedical Optics Express","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Eye Institute; Foundation Fighting Blindness; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC; Research to Prevent Blindness","keywords":"Optical coherence tomography; Adaptive optics; Wavefront; Optics; Retinal; Deformable mirror; Image quality; Computer science; Preclinical imaging; Optical tomography; Wavefront sensor; Image processing; Computer vision; Artificial intelligence; Physics; In vivo; Image (mathematics); Ophthalmology; Medicine; Biology","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":[0.0004722983,0.0005764824,0.0002434219,0.0005506218,0.0002429158,0.0004195447,0.0004966742,0.0004493992,0.00104067],"category_scores_gemma":[0.0002815792,0.0003365432,0.0002804261,0.0003259413,0.000291359,0.0004683459,0.0002435294,0.0007958304,0.0002908741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003379432,"about_ca_system_score_gemma":0.0003651402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008741661,"about_ca_topic_score_gemma":0.002585077,"domain_scores_codex":[0.9998236,0.00002792216,0.00001461514,0.00003820845,0.00007635057,0.00001926548],"domain_scores_gemma":[0.9997351,0.00004382162,0.00009456908,0.00004940077,0.00003316516,0.00004390625],"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":[0.00007335529,0.00002777721,0.0001150441,0.00002914515,0.000005415649,0.00003097622,0.00001162014,0.0003544249,0.9962639,0.0004714678,0.000113053,0.00250374],"study_design_scores_gemma":[0.00002673689,0.0002101043,0.0009176136,0.000007957226,0.00001445182,0.0001906955,0.000006071404,0.007076809,0.9886592,0.0001515937,0.002729986,0.000008747617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5831543,0.002091917,0.4046624,0.0009817143,0.0001863844,0.0004376064,0.001892198,0.001934899,0.004658553],"genre_scores_gemma":[0.6067041,0.00205376,0.3842287,0.0002545713,0.0000402262,0.0005077608,0.0008650335,0.000277224,0.005068561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00104067,"threshold_uncertainty_score":0.003481388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178005327793567,"score_gpt":0.2379645384859982,"score_spread":0.2261844852080626,"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."}}