{"id":"W2620803373","doi":"","title":"Sharing Information from Personal Digital Notes using Word-Scale Visualizations","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Context (archaeology); Visualization; World Wide Web; Set (abstract data type); Scale (ratio); Word (group theory); Data visualization; Raw data; Information sharing; Information visualization; Data science; Data sharing; Information retrieval; Artificial intelligence; Linguistics","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001897456,0.0003227941,0.0003209358,0.0003153508,0.0003613041,0.003736689,0.002228629,0.0002396962,0.00006951382],"category_scores_gemma":[0.00141575,0.0003642174,0.0001571086,0.0006781747,0.0001265909,0.002144097,0.003697553,0.0003920458,0.0001027953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921937,"about_ca_system_score_gemma":0.0004999142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008520075,"about_ca_topic_score_gemma":0.000279367,"domain_scores_codex":[0.9969273,0.0007653001,0.0006544778,0.0006646549,0.0006738169,0.0003144611],"domain_scores_gemma":[0.9942501,0.0004829494,0.0006143043,0.001751079,0.002638703,0.0002628246],"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":[0.00002377392,0.002242204,0.03361267,0.0005351966,0.0005537677,0.00001409993,0.174414,0.006761701,0.0009395448,0.4585183,0.008522241,0.3138625],"study_design_scores_gemma":[0.0002990077,1.919951e-7,0.0005140022,0.0007036323,0.00002862598,0.000003886342,0.0001684796,0.979315,0.001328716,0.008977901,0.008235959,0.0004245774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02234926,0.0001574531,0.9670761,0.001758282,0.0002818951,0.0002162998,0.0004137775,0.0003873222,0.007359643],"genre_scores_gemma":[0.7493533,0.0001404703,0.2387174,0.0004207114,0.00008920016,0.0000291176,0.0096719,0.00005930784,0.001518652],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9725533,"threshold_uncertainty_score":0.999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03473076662963628,"score_gpt":0.2785753983767565,"score_spread":0.2438446317471203,"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."}}