{"id":"W4302560500","doi":"","title":"Personal Visualization: Exploring Data in Everyday Life","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Calgary","funders":"","keywords":"Everyday life; Visualization; Computer science; Psychology; Human–computer interaction; Epistemology; Artificial intelligence; Philosophy","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":[0.002854518,0.001617366,0.001216347,0.003746608,0.001469911,0.010528,0.001289926,0.001875038,0.05811652],"category_scores_gemma":[0.01296611,0.0006474497,0.001175705,0.005518917,0.001618028,0.007242418,0.008829664,0.001609623,0.008109402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005615141,"about_ca_system_score_gemma":0.001080315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720963,"about_ca_topic_score_gemma":0.002579526,"domain_scores_codex":[0.9986563,0.0005179253,0.0001037341,0.0003031092,0.0003384201,0.00008057831],"domain_scores_gemma":[0.9907137,0.004808345,0.0003261154,0.002547031,0.0006760014,0.0009287532],"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":[0.001160352,0.0002626131,0.007109098,0.004263858,0.0004261427,0.001444839,0.02581653,0.004694853,0.02584367,0.04408374,0.2788448,0.6060495],"study_design_scores_gemma":[0.0002272216,0.0002110518,0.01326368,0.001353105,0.0003239483,0.00207345,0.01176489,0.0493218,0.01420041,0.2382319,0.6687143,0.0003141821],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04755404,0.004899239,0.8248453,0.009205547,0.001732396,0.0008460296,0.01963493,0.05900501,0.03227744],"genre_scores_gemma":[0.331028,0.008060003,0.5963851,0.001688522,0.001557099,0.001881289,0.0143767,0.01508649,0.0299368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05811652,"threshold_uncertainty_score":0.1944191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1103667066270298,"score_gpt":0.3276874845111887,"score_spread":0.2173207778841589,"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."}}