{"id":"W3168092689","doi":"10.2196/30115","title":"Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Learning Styles and Cognitive Differences","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Feature selection; Artificial intelligence; Random forest; Computer science; Naive Bayes classifier; Health promotion; Natural language processing; Public health; Support vector machine; Medicine; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00173313,0.0005722154,0.0004409727,0.002477252,0.0002355294,0.0009295178,0.0003632965,0.0005055662,0.001257387],"category_scores_gemma":[0.00762433,0.0001619769,0.0007023133,0.0008230653,0.000193637,0.000511246,0.0003454028,0.0005488976,0.0004720827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000478334,"about_ca_system_score_gemma":0.0006245223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001698595,"about_ca_topic_score_gemma":0.002590196,"domain_scores_codex":[0.9993553,0.0002422007,0.00008746557,0.0001548218,0.0001141918,0.00004600114],"domain_scores_gemma":[0.9940543,0.004237404,0.0006389621,0.0001780349,0.0007239913,0.0001673782],"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":[0.0009161889,0.001208596,0.3853204,0.0002848101,0.000280274,0.0002602645,0.0004649138,0.02755625,0.00909871,0.0003401859,0.002771227,0.5714982],"study_design_scores_gemma":[0.00008622969,0.0006256934,0.2811757,0.0001202647,0.0001646321,0.0003347548,0.0005004411,0.7037898,0.01075429,0.001223379,0.001177522,0.00004746292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575362,0.0003066048,0.03886875,0.0001954316,0.00003545544,0.0001874435,0.0007230216,0.0004598711,0.001687238],"genre_scores_gemma":[0.9546863,0.00008796557,0.0434908,0.00003788819,0.00002332632,0.0001208354,0.0008895917,0.00001421142,0.0006492045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002477252,"threshold_uncertainty_score":0.009165764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957814947937367,"score_gpt":0.3411512338393892,"score_spread":0.3215730843600155,"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."}}