{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02415447,0.0004595608,0.0006721645,0.00388332,0.0007447057,0.001311227,0.0008985311,0.000822451,0.002707036],"category_scores_gemma":[0.05643208,0.0002976439,0.0009271565,0.001382424,0.001187888,0.0009483855,0.001381743,0.0005060647,0.0005737623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147685,"about_ca_system_score_gemma":0.001083778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003837852,"about_ca_topic_score_gemma":0.004130701,"domain_scores_codex":[0.9818568,0.006111284,0.002333035,0.001848576,0.007393307,0.0004569741],"domain_scores_gemma":[0.9318184,0.02284467,0.005361121,0.007699149,0.03185651,0.0004200717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0037358,0.0003312048,0.330994,0.001319223,0.0009908159,0.0005577599,0.001914078,0.009862863,0.1348123,0.006488826,0.004031696,0.5049614],"study_design_scores_gemma":[0.0003002273,0.001507572,0.5114014,0.0002955341,0.0006121708,0.004116151,0.0009937432,0.2214477,0.2380708,0.006406682,0.01450962,0.0003384067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4756154,0.001733391,0.5101132,0.0004603105,0.0001403724,0.00166887,0.001319553,0.001148584,0.007800306],"genre_scores_gemma":[0.811339,0.0003578941,0.183981,0.0001770549,0.0000644972,0.0008171188,0.0007998564,0.0002442824,0.002219268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02415447,"threshold_uncertainty_score":0.1277426,"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."}}