{"id":"W3188759586","doi":"10.1177/14738716211033246","title":"MuzLink: Connected beeswarm timelines for visual analysis of musical adaptations and artist relationships","year":2021,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Timeline; Computer science; Visualization; Musical; Set (abstract data type); Domain (mathematical analysis); Exploratory search; Human–computer interaction; Graph; Information visualization; World Wide Web; Data science; Artificial intelligence; Theoretical computer science; Visual arts","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003827428,0.0001161074,0.0002387737,0.0006365853,0.0002304056,0.0002557558,0.000146835,0.00009227042,0.00003756662],"category_scores_gemma":[0.001216334,0.0001256681,0.00008843601,0.002871625,0.00004482124,0.00205251,0.00008137021,0.00005463349,0.00001059229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002461457,"about_ca_system_score_gemma":0.000131985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009039605,"about_ca_topic_score_gemma":0.00004232866,"domain_scores_codex":[0.9985404,0.0001070929,0.0007418353,0.0001881153,0.0002888868,0.0001336717],"domain_scores_gemma":[0.9977061,0.0003161404,0.0003712003,0.0002463205,0.001277818,0.00008245153],"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":[0.00001040437,0.0001378879,0.002723341,0.00007009983,0.0003028114,4.378544e-7,0.003090033,0.01039159,0.0003757251,0.9733106,0.001316586,0.008270436],"study_design_scores_gemma":[0.0003731117,0.00003754386,0.01547417,0.00001436789,0.0002416094,0.000002121561,0.0004080888,0.9723457,0.001021973,0.0006637241,0.009273789,0.0001438515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01583845,0.00002746771,0.9830238,0.0003332628,0.0001027608,0.0001786301,0.0001136329,0.0001162645,0.0002657248],"genre_scores_gemma":[0.9659111,0.00005913098,0.02647472,0.0007097255,0.00003709356,0.00002802599,0.006639258,0.000008703039,0.0001322147],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.972647,"threshold_uncertainty_score":0.5124597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03593310119932884,"score_gpt":0.3241047055257082,"score_spread":0.2881716043263793,"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."}}