{"id":"W2545503847","doi":"10.3390/informatics3040020","title":"Supporting Sensemaking of Complex Objects with Visualizations: Visibility and Complementarity of Interactions","year":2016,"lang":"en","type":"article","venue":"Informatics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Sensemaking; Computer science; Human–computer interaction; Complementarity (molecular biology); Visualization; Visual analytics; Usability; Visibility; Representation (politics); Information visualization; Data science; Knowledge management; Artificial intelligence","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":[0.0002641575,0.00006977605,0.0001532898,0.0001012514,0.00005875508,0.00003847828,0.0001826814,0.0000133592,0.00003085055],"category_scores_gemma":[0.0001036978,0.00004695728,0.0000191536,0.0002513594,0.00009215048,0.0007985222,0.0001765617,0.00002687007,0.000001827616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001755998,"about_ca_system_score_gemma":0.00004799394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001997115,"about_ca_topic_score_gemma":0.00006390224,"domain_scores_codex":[0.9990032,0.00002545273,0.0006006319,0.00006778444,0.0001914488,0.0001114712],"domain_scores_gemma":[0.9988102,0.0001538738,0.0005013937,0.0002781464,0.0002156695,0.00004071734],"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":[0.00004093165,0.0006833847,0.2225849,0.001829216,0.000299105,0.000003733814,0.04347092,0.0003953782,0.008952446,0.6455323,0.002367212,0.07384057],"study_design_scores_gemma":[0.002158405,0.0004331573,0.03371976,0.0007696723,0.00006568445,0.00006507341,0.006711261,0.9267656,0.01671352,0.00412542,0.007967164,0.0005052372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05683346,0.000002131762,0.9419445,0.00007864411,0.00002529581,0.00009755956,0.00004593775,0.00003154866,0.0009409571],"genre_scores_gemma":[0.9427005,0.000005024384,0.05711953,0.0001316261,0.00000396984,9.221881e-7,0.00001651061,0.000002615594,0.00001926984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9263703,"threshold_uncertainty_score":0.1914862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04186532548949052,"score_gpt":0.3709616049255003,"score_spread":0.3290962794360098,"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."}}