{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01704558,0.0002293716,0.0003715918,0.0002780635,0.0005298608,0.0007152589,0.002704435,0.0001661149,0.0003038138],"category_scores_gemma":[0.0079014,0.0002600453,0.000107361,0.0006530446,0.0002951572,0.0008032121,0.003635813,0.0005108555,0.00004912964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001782457,"about_ca_system_score_gemma":0.001258473,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01370797,"about_ca_topic_score_gemma":0.04175429,"domain_scores_codex":[0.989594,0.007736411,0.0005160043,0.0008890152,0.0008604606,0.0004041094],"domain_scores_gemma":[0.9950048,0.001068881,0.0003809422,0.002153418,0.001062403,0.0003295216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002208551,0.00134278,0.05906426,0.0002862477,0.0002587129,0.00002338547,0.4251656,0.0002513345,0.0001502317,0.4036307,0.01950749,0.09029714],"study_design_scores_gemma":[0.001468145,6.140941e-7,0.02213295,0.007438553,0.0002532392,0.000003041835,0.02448592,0.188872,0.0005274895,0.01518645,0.7372454,0.002386246],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4191509,0.004653676,0.09526169,0.04011524,0.001472793,0.001245176,0.0009296503,0.0007067589,0.4364641],"genre_scores_gemma":[0.9798892,0.002813068,0.007150115,0.0001322655,0.0001362286,0.00006048843,0.003424769,0.00004154893,0.006352286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7177379,"threshold_uncertainty_score":0.9999852,"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."}}