{"id":"W2102450237","doi":"10.1109/mcg.2015.52","title":"Understanding Digital Note-Taking Practice for Visualization","year":2015,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Computer science; Visualization; Variety (cybernetics); Reflection (computer programming); Digital content; Digital library; World Wide Web; Data visualization; Human–computer interaction; Data science; Multimedia; Artificial intelligence; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0007520678,0.00008144383,0.00009482916,0.0002285094,0.0002544533,0.0009955029,0.0002179908,0.00003980683,0.00000243036],"category_scores_gemma":[0.0001009918,0.00007061394,0.00004700452,0.000497318,0.00005238364,0.001098928,0.00005225709,0.00004637365,0.00002411564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002710096,"about_ca_system_score_gemma":0.00002515029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002335958,"about_ca_topic_score_gemma":0.00000281023,"domain_scores_codex":[0.9989146,0.00001716233,0.0003015305,0.0002167728,0.0004349459,0.0001149716],"domain_scores_gemma":[0.9985578,0.000505104,0.0002601795,0.0001918057,0.0003816116,0.0001035084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009791087,0.00005667952,0.00113547,0.000004282106,0.000008865175,2.200755e-7,0.0004847027,0.0002429944,0.000005092831,0.9679344,0.01360644,0.01651103],"study_design_scores_gemma":[0.0005772735,0.00008217684,0.0005484864,0.000007737092,0.00003403562,0.000006974398,0.001106262,0.2475113,0.00001223691,0.1616369,0.5882599,0.0002167611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001842138,0.00001777096,0.9954168,0.0006067304,0.0001527745,0.0003852796,0.00002417926,0.00004914442,0.001505178],"genre_scores_gemma":[0.9919551,0.00001247105,0.006631347,0.0008833524,0.0002485617,0.0001074163,0.00004244639,0.00000878071,0.0001105324],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.990113,"threshold_uncertainty_score":0.9599658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.583382249347669,"score_gpt":0.4898153160920119,"score_spread":0.09356693325565713,"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."}}