{"id":"W4296077872","doi":"10.21203/rs.3.rs-2057322/v1","title":"Global Trends and Prospects in Research of Artificial Cornea Over Past 20 Years: A Bibliometric and Visualized Analysis","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Corneal surgery and disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cornea; China; Perspective (graphical); Geography; Computer science; Artificial intelligence; Medicine; Ophthalmology; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004290786,0.0003444308,0.0008604074,0.09854366,0.0006638236,0.004160849,0.0003342888,0.0004194182,0.003291003],"category_scores_gemma":[0.01521493,0.0001195154,0.0009447819,0.1809271,0.0005796404,0.002590549,0.001295825,0.0003356083,0.0005271331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001558567,"about_ca_system_score_gemma":0.002222809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004147023,"about_ca_topic_score_gemma":0.004574958,"domain_scores_codex":[0.995776,0.0007615545,0.0008542283,0.0004726447,0.001919784,0.0002157592],"domain_scores_gemma":[0.979273,0.01010166,0.004842849,0.000617536,0.004614003,0.0005509255],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000267683,0.00008083247,0.6670083,0.0137957,0.001262266,0.0007563726,0.00631901,0.002221186,0.002163989,0.009286187,0.01446181,0.2823766],"study_design_scores_gemma":[0.00001542477,0.0001183216,0.9249799,0.002244524,0.0007820369,0.0009796916,0.007945533,0.002079441,0.0009461311,0.002602867,0.05725039,0.00005560938],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7732538,0.1170247,0.00310629,0.004795439,0.0002505519,0.0002677806,0.05391297,0.0004184316,0.04697004],"genre_scores_gemma":[0.9316525,0.04751873,0.003796305,0.0001259384,0.0003052342,0.0001640266,0.01456878,0.00004436709,0.001824179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9957092,"threshold_uncertainty_score":0.02269214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1679558855161167,"score_gpt":0.5116451288237108,"score_spread":0.3436892433075941,"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."}}