{"id":"W2027288560","doi":"10.1097/opx.0b013e3182120514","title":"Waterloo Eye Study: Data Abstraction and Population Representation","year":2011,"lang":"en","type":"article","venue":"Optometry and Vision Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Representation (politics); Abstraction; Computer science; Population; Optometry; Medicine; Political science; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01954822,0.000437763,0.0008069132,0.005940692,0.00126134,0.002057706,0.001625112,0.0004496634,0.003011295],"category_scores_gemma":[0.0582898,0.0006901862,0.0007864254,0.01293734,0.0008928482,0.001469908,0.002117005,0.0005064653,0.0005305404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008024631,"about_ca_system_score_gemma":0.01312808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3902558,"about_ca_topic_score_gemma":0.3929055,"domain_scores_codex":[0.9611874,0.01185222,0.008002381,0.002721798,0.01484631,0.001389954],"domain_scores_gemma":[0.963134,0.006924998,0.01146424,0.003953813,0.01333406,0.001188911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000499495,0.00004914693,0.9740276,0.0004800264,0.0001940392,0.00008558266,0.001381457,0.0001113798,0.0002033731,0.0003374861,0.007930819,0.01469955],"study_design_scores_gemma":[0.0001242838,0.00009173487,0.9874331,0.0003008511,0.00007469289,0.0000860833,0.0008410495,0.0003571313,0.0001602588,0.00009073049,0.01041913,0.00002092425],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8683742,0.004316749,0.007802156,0.002132048,0.0001191123,0.01178985,0.09150477,0.0001906334,0.0137704],"genre_scores_gemma":[0.9230793,0.001363833,0.007666766,0.0008308318,0.0001196488,0.01126575,0.05245548,0.00008520208,0.003133271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3902558,"threshold_uncertainty_score":0.7759686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07392387330951153,"score_gpt":0.4916021544340156,"score_spread":0.417678281124504,"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."}}