{"id":"W3216874313","doi":"","title":"Personal Patient-Generated Data Visualizations for Diabetes Patients","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Computer science; Tracking (education); Data visualization; Process (computing); Health care; Patient data; Sample (material); Data science; Visualization; Internet privacy; Medicine; Psychology; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002601827,0.0004201463,0.00037778,0.000453458,0.0007188549,0.0007324571,0.004331316,0.0004286715,0.00007648146],"category_scores_gemma":[0.002976291,0.0004628412,0.0001303274,0.0007271417,0.0003391013,0.0007104285,0.00571336,0.0005119326,0.00005189231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000197638,"about_ca_system_score_gemma":0.0003443244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007057704,"about_ca_topic_score_gemma":0.0002930429,"domain_scores_codex":[0.9945952,0.002195593,0.00071203,0.001518261,0.0004820717,0.0004968285],"domain_scores_gemma":[0.9859208,0.0008730174,0.000922939,0.004042872,0.008134416,0.0001058972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004910936,0.006637822,0.02700397,0.000740635,0.001205213,0.000005305809,0.03924068,0.00007635864,0.01018342,0.5065969,0.1885133,0.2197473],"study_design_scores_gemma":[0.001270125,0.000007021126,0.003382609,0.001608974,0.00008884224,0.000003345194,0.00008212512,0.7850089,0.1265701,0.01313167,0.06752034,0.001325983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1953153,0.000128116,0.7920146,0.005338956,0.001181185,0.001028896,0.0005979841,0.0006357259,0.003759208],"genre_scores_gemma":[0.7816688,0.00004115298,0.2046715,0.000530734,0.00007784037,0.0003539997,0.01081725,0.00008120781,0.001757451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7849326,"threshold_uncertainty_score":0.9997823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03276688013242737,"score_gpt":0.2762443321474944,"score_spread":0.2434774520150671,"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."}}