{"id":"W2993646948","doi":"","title":"Comparison of Traditional Scoring and Neural Network Analysis of Farnsworth-Munsell 100 Hue Test Data","year":2007,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Advanced Decision-Making Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Test (biology); Hue; Computer science; Natural language processing; Statistics; Mathematics; Biology; Botany","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":[0.001999616,0.0004799378,0.0003164448,0.002420147,0.0002130277,0.0007806423,0.0005080204,0.0004352576,0.002829699],"category_scores_gemma":[0.009546775,0.00009457266,0.0003141431,0.001056395,0.0001619602,0.001076494,0.0004334804,0.0002460484,0.0007064446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005186057,"about_ca_system_score_gemma":0.0004380274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008781587,"about_ca_topic_score_gemma":0.01545023,"domain_scores_codex":[0.9992024,0.0002725681,0.00009153311,0.0001107006,0.000264924,0.00005794366],"domain_scores_gemma":[0.9931145,0.004056233,0.0002233261,0.0002669121,0.002224955,0.0001140844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004076914,0.0003616099,0.0994288,0.0002779249,0.0003897631,0.0001862806,0.0002516397,0.03964087,0.02010998,0.0009880611,0.003351019,0.8309371],"study_design_scores_gemma":[0.00006672736,0.0003825884,0.1325925,0.00004606918,0.0001468919,0.0002432434,0.0003026721,0.8512281,0.01228418,0.001433326,0.001225317,0.000048392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8835449,0.0006067244,0.1073142,0.0002113452,0.0001102954,0.0001090571,0.001133927,0.001215776,0.005753738],"genre_scores_gemma":[0.9624663,0.0002355102,0.03361794,0.00004049389,0.00003663422,0.0000437916,0.000923423,0.0001029052,0.002533059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008781587,"threshold_uncertainty_score":0.01746094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1640775365088832,"score_gpt":0.4330258324408775,"score_spread":0.2689482959319943,"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."}}