{"id":"W2106687164","doi":"10.1109/tvcg.2014.2359887","title":"Personal Visualization and Personal Visual Analytics","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":240,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; Simon Fraser University; University of Calgary; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual analytics; Computer science; Data science; Visualization; Data visualization; Analytics; Context (archaeology); Set (abstract data type); Cultural analytics; Vocabulary; Information visualization; Human–computer interaction; World Wide Web; The Internet; Semantic analytics; Artificial intelligence","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":[0.000324852,0.0002991983,0.0002522542,0.000887168,0.0005944425,0.0003181509,0.0002097058,0.0002276019,0.00002106993],"category_scores_gemma":[0.000008443606,0.0003144779,0.00007684545,0.001105841,0.000228958,0.0007004577,0.0000124363,0.0003177315,0.00001181419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004631821,"about_ca_system_score_gemma":0.00003576668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001092297,"about_ca_topic_score_gemma":0.00002044299,"domain_scores_codex":[0.9981282,0.0001887967,0.0003766707,0.0006366899,0.0003894656,0.0002801202],"domain_scores_gemma":[0.9990435,0.0001422335,0.0001681274,0.0001993386,0.0003358356,0.0001110111],"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":[0.00002496439,0.0002832511,0.0003079558,0.00004261505,0.00008463087,0.000002663073,0.002489071,0.0000620865,0.000132487,0.9767359,0.0002176074,0.01961671],"study_design_scores_gemma":[0.0006275956,0.0005755649,0.001023176,0.00004620201,0.00002979388,0.00006503613,0.00008568656,0.9930622,0.001666272,0.001171419,0.001314082,0.0003329848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08044007,0.0000178309,0.9181448,0.0001340583,0.0006520386,0.0001659802,0.000004176917,0.0003694869,0.00007156671],"genre_scores_gemma":[0.9967601,0.00008799326,0.001047152,0.001842134,0.0001045868,0.00001818554,0.00001353874,0.00002651899,0.00009973144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9930001,"threshold_uncertainty_score":0.9999307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854176604427159,"score_gpt":0.2870826779491683,"score_spread":0.2685409119048967,"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."}}