{"id":"W4312116152","doi":"10.2196/preprints.45268","title":"Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Computer science; Terminology; Usability; World Wide Web; Artificial intelligence; Data science; Knowledge management; Information retrieval; Natural language processing","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":[0.01249972,0.0008595368,0.0005752422,0.003941811,0.0007269942,0.002637331,0.001271638,0.0008248299,0.002146554],"category_scores_gemma":[0.04258544,0.0005329918,0.001388927,0.002292408,0.0007669568,0.004791966,0.002832714,0.001226138,0.0007072166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130852,"about_ca_system_score_gemma":0.00178434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008625401,"about_ca_topic_score_gemma":0.01184524,"domain_scores_codex":[0.9930543,0.004417195,0.0007040506,0.0009460813,0.000730785,0.0001476345],"domain_scores_gemma":[0.9152414,0.07691569,0.001407488,0.002597285,0.003429949,0.0004082896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001010756,0.00285384,0.03038873,0.006598934,0.0006441033,0.00103589,0.03048948,0.01510593,0.02974186,0.004581472,0.01952736,0.8580216],"study_design_scores_gemma":[0.0008025313,0.004619897,0.1060883,0.00305745,0.00146352,0.001969167,0.03969354,0.6144916,0.07510022,0.02481097,0.1269999,0.0009029602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6998506,0.001014754,0.2599367,0.001366936,0.0001395333,0.006631491,0.007993252,0.01709812,0.005968749],"genre_scores_gemma":[0.39913,0.0005248978,0.5871571,0.0003388339,0.00002212388,0.002667525,0.008051101,0.0006781847,0.001430215],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01249972,"threshold_uncertainty_score":0.0661056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02299959624589169,"score_gpt":0.2972719642577111,"score_spread":0.2742723680118194,"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."}}