{"id":"W2112648711","doi":"","title":"Corneal confocal microscopy image quality analysis and validity assessment","year":2010,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Ocular Surface and Contact Lens","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"LMC Diabetes & Endocrinology (Canada)","funders":"","keywords":"Confocal microscopy; Artificial intelligence; Confocal; Cornea; Computer vision; Computer science; Support vector machine; Image quality; Microscopy; Biomedical engineering; Pattern recognition (psychology); Optics; Medicine; Image (mathematics); Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001914787,0.000111297,0.000360188,0.0002033441,0.0002727545,0.00003205161,0.0002445597,0.0001040238,0.0003709545],"category_scores_gemma":[0.00008645566,0.00009286644,0.0001909888,0.0004248699,0.00082534,0.0001524286,0.0002594303,0.0007795248,0.00004620188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006208885,"about_ca_system_score_gemma":0.0001721417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001807115,"about_ca_topic_score_gemma":0.00009415159,"domain_scores_codex":[0.9982429,0.0003701111,0.0001511661,0.0003058484,0.0006095134,0.0003204311],"domain_scores_gemma":[0.9983754,0.0003458312,0.00006939183,0.0006349332,0.0003647172,0.0002096832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004460911,0.0003052267,0.7250069,0.0001694393,0.0007089037,0.0001166671,0.008683166,6.117991e-7,0.2619639,0.0007480549,0.0003308032,0.001520236],"study_design_scores_gemma":[0.001096414,0.000223607,0.9684829,0.00002162917,0.0003800223,0.0000035029,0.01218283,0.0002505625,0.01264686,0.0001020709,0.004500897,0.0001087303],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951594,0.00002250251,0.001921165,0.002087847,0.00003705901,0.0002831395,0.00001352423,0.00002114881,0.0004542453],"genre_scores_gemma":[0.9969274,0.0001309812,0.002709425,0.00005108552,0.00003562538,6.543236e-7,0.00001983634,0.000008232661,0.0001167741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.249317,"threshold_uncertainty_score":0.4061691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08999964211386881,"score_gpt":0.3871644996561713,"score_spread":0.2971648575423025,"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."}}