{"id":"W3199112149","doi":"10.1080/08164622.2021.1973866","title":"Bibliometric analysis of the keratoconus literature","year":2021,"lang":"en","type":"article","venue":"Clinical and Experimental Optometry","topic":"Corneal surgery and disorders","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Keratoconus; Scopus; Bibliometrics; Index (typography); Subject (documents); Library science; Impact factor; Medicine; MEDLINE; Optometry; Ophthalmology; Political science; Computer science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0002134036,0.00008906257,0.0004338748,0.007387151,0.00004946029,0.00002600062,0.00005349706,0.000111151,0.0004060457],"category_scores_gemma":[0.0003770705,0.00005564749,0.0004890905,0.1327155,0.0001838916,0.00004615453,0.0001229026,0.000177944,0.000003735392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006416513,"about_ca_system_score_gemma":0.00003949597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003152682,"about_ca_topic_score_gemma":2.917102e-7,"domain_scores_codex":[0.9989905,0.00007076529,0.0003861499,0.000239058,0.0001973188,0.0001162529],"domain_scores_gemma":[0.9991562,0.0002732779,0.00006923818,0.0002743052,0.00008586698,0.0001411047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001191906,0.0006331669,0.9874393,0.00002309041,0.000558616,0.00002492922,0.00006374905,8.809947e-7,0.002977625,0.00005348446,0.0004634576,0.007642474],"study_design_scores_gemma":[0.0007942848,0.00009455773,0.9734914,0.0000439604,0.0003865373,0.0000170109,0.0002518134,0.0000824497,0.02279815,0.00001043202,0.001959586,0.00006981744],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796663,0.01749282,0.000005844753,0.0003248582,0.0002343917,0.00008458715,0.00001253098,0.00001083557,0.002167818],"genre_scores_gemma":[0.9976525,0.0003816937,0.00009649595,0.001274171,0.00005067337,0.000004934441,0.00002251056,0.00000520757,0.0005118684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1253284,"threshold_uncertainty_score":0.885719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03211646747737853,"score_gpt":0.4101061812742237,"score_spread":0.3779897137968452,"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."}}