{"id":"W1996453211","doi":"10.1117/12.2042010","title":"Evaluation of OCT for quantitative in-vivo measurements of changes in neural tissue scattering in longitudinal studies of retinal degeneration in mice","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simon Fraser University; University of California, Davis; Durham University; Research to Prevent Blindness","keywords":"Optical coherence tomography; Retinal; Optics; Brightness; Normalization (sociology); Preclinical imaging; Light scattering; Materials science; Retina; Biomedical engineering; Scattering; Computer science; Ophthalmology; Medicine; In vivo; Physics; Biology","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":[0.002381932,0.0002141186,0.0005073056,0.0003757991,0.00001466489,0.00001301011,0.0003851905,0.0001183954,0.000003315804],"category_scores_gemma":[0.001157087,0.0002128371,0.0001571,0.0006783932,0.0001753941,0.0003358658,0.00005630583,0.0001822317,8.101989e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002305625,"about_ca_system_score_gemma":0.00002148206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002897968,"about_ca_topic_score_gemma":0.00004759568,"domain_scores_codex":[0.9978313,1.983459e-7,0.000944231,0.0002596058,0.0007032016,0.0002614634],"domain_scores_gemma":[0.9975418,0.0002536812,0.0003154169,0.0000475636,0.001807784,0.00003377562],"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":[0.00008929515,0.0001436383,0.01469715,0.001560642,0.0001157494,1.482085e-8,0.0007879678,0.02053169,0.9271009,0.03427729,0.00002261437,0.0006730505],"study_design_scores_gemma":[0.001564764,0.0003750957,0.01941691,0.000959705,0.0001052161,9.457435e-7,0.001385487,0.3168954,0.6576868,0.001388671,0.000007928681,0.0002130554],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978942,0.0003840946,0.00009311975,0.000277702,0.00007645983,0.0009550554,0.00002348655,0.00001477477,0.0002811465],"genre_scores_gemma":[0.960865,0.00006398445,0.0385677,0.000004017869,0.00004031928,0.0004272025,0.000003535497,0.00002639926,0.000001872705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2963637,"threshold_uncertainty_score":0.8679244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0666135792745645,"score_gpt":0.3133536330516902,"score_spread":0.2467400537771257,"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."}}