{"id":"W2911014696","doi":"10.1111/cgf.13596","title":"MyEvents: A Personal Visual Analytics Approach for Mining Key Events and Knowledge Discovery in Support of Personal Reminiscence","year":2019,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Information and Communication Technologies; Engineering and Physical Sciences Research Council; Horizon 2020 Framework Programme; European Commission; Queen's University; Queen's University Belfast","keywords":"Reminiscence; Event (particle physics); Recall; Computer science; Analytics; Human–computer interaction; Process (computing); Visual analytics; Data science; Visualization; World Wide Web; Psychology; Artificial intelligence; Cognitive psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0008663487,0.0008765081,0.0005321996,0.004632683,0.0004232611,0.001739007,0.001154491,0.0006035261,0.003522478],"category_scores_gemma":[0.003165163,0.0003253732,0.00096739,0.002164881,0.0002360953,0.001728974,0.001535942,0.0006950022,0.001034721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003794668,"about_ca_system_score_gemma":0.0004811085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003012373,"about_ca_topic_score_gemma":0.007056903,"domain_scores_codex":[0.9995549,0.00009299883,0.00004315828,0.0001548424,0.0001165497,0.0000375429],"domain_scores_gemma":[0.9988662,0.000501755,0.0001563826,0.0001873885,0.0001678643,0.0001203955],"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":[0.001622397,0.0009638094,0.02377022,0.001421151,0.000614838,0.0008757435,0.003315262,0.02030667,0.03353625,0.009936987,0.04246987,0.8611668],"study_design_scores_gemma":[0.000208017,0.000594532,0.02942815,0.000232737,0.0003324318,0.001116909,0.003588059,0.813719,0.02950628,0.03873624,0.08236037,0.0001772848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1441031,0.001737742,0.8025373,0.0007799601,0.0001428781,0.0008101941,0.01619805,0.02767198,0.006018779],"genre_scores_gemma":[0.4020109,0.0007626229,0.5789835,0.0001717198,0.00009614712,0.0004922969,0.01352996,0.0004759598,0.003476855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004632683,"threshold_uncertainty_score":0.0117839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02268000327881026,"score_gpt":0.2931121402544004,"score_spread":0.2704321369755902,"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."}}