{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006874848,0.0008034735,0.0004535165,0.002298446,0.001658068,0.0102768,0.001387867,0.001384008,0.00888875],"category_scores_gemma":[0.01819484,0.0005627348,0.0007462512,0.002287234,0.004191757,0.01064059,0.005959681,0.001829416,0.001275368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001221552,"about_ca_system_score_gemma":0.001379967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001727316,"about_ca_topic_score_gemma":0.001696256,"domain_scores_codex":[0.9944829,0.003231943,0.0003255781,0.0007119791,0.0009666902,0.000280951],"domain_scores_gemma":[0.9874435,0.006432611,0.0008236911,0.003151851,0.001398202,0.0007501946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002816275,0.0001624429,0.009329049,0.001647549,0.0001288562,0.0005594862,0.0498341,0.007476147,0.008463497,0.5340278,0.03216997,0.3559195],"study_design_scores_gemma":[0.00005036776,0.0001522001,0.00432581,0.0008671536,0.0001110387,0.001527504,0.0149225,0.02394736,0.006229199,0.461119,0.4866157,0.0001322917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05143126,0.004241879,0.848411,0.01142614,0.0004179467,0.0004146416,0.0008376542,0.004758378,0.07806113],"genre_scores_gemma":[0.5721072,0.003266088,0.4060586,0.001103924,0.0003364704,0.0006314887,0.001020226,0.001098785,0.01437716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0102768,"threshold_uncertainty_score":0.03635806,"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."}}