{"id":"W7065964900","doi":"","title":"Health information network representation learning","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ontology; Graph; Knowledge representation and reasoning; Domain knowledge; Representation (politics); Feature learning; Domain (mathematical analysis); Artificial neural network","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":[0.0007926436,0.0005421418,0.000357775,0.001302884,0.0003391566,0.001075072,0.000820654,0.0009000165,0.00682466],"category_scores_gemma":[0.005461867,0.0002266664,0.0006396851,0.001559487,0.0003460243,0.001753494,0.0009633458,0.001482201,0.001424306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001280688,"about_ca_system_score_gemma":0.0008405937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005783727,"about_ca_topic_score_gemma":0.005903315,"domain_scores_codex":[0.9995006,0.0001573778,0.00002137436,0.000170972,0.00009726306,0.00005246663],"domain_scores_gemma":[0.9990178,0.0005226666,0.00008350066,0.0001578057,0.000178279,0.00003994152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000129967,0.000151458,0.003760248,0.0002524414,0.0001335358,0.0001646023,0.000208028,0.2053513,0.001320265,0.11924,0.06247639,0.6068118],"study_design_scores_gemma":[0.00001305499,0.00002908639,0.0008201249,0.00007418608,0.00003636503,0.000082443,0.00004447318,0.8683794,0.0007200617,0.1130968,0.0166916,0.0000124166],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02311123,0.002828807,0.9378008,0.007063889,0.0004220479,0.0002033789,0.00462871,0.001705122,0.02223599],"genre_scores_gemma":[0.652528,0.004722537,0.2967396,0.001897243,0.0006651368,0.0005479454,0.01510465,0.0001917994,0.02760298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00682466,"threshold_uncertainty_score":0.02283078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725121211394399,"score_gpt":0.2369626744797424,"score_spread":0.2197114623657984,"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."}}