{"id":"W4206873235","doi":"10.1007/978-3-030-68766-3","title":"Graph drawing and network visualization 28th international symposium, GD 2020, Vancouver, BC, Canada, September 16–18, 2020 : revised selected papers","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Library science; History; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002236572,0.0004427793,0.0004906683,0.0001174308,0.0004832122,0.001584899,0.001933449,0.0002523419,0.0003827972],"category_scores_gemma":[0.0008529166,0.0005082865,0.0001483622,0.001455003,0.0001152103,0.0004569314,0.002506207,0.0005209568,0.000005784119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003444647,"about_ca_system_score_gemma":0.001239419,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02185397,"about_ca_topic_score_gemma":0.349402,"domain_scores_codex":[0.9937556,0.002891062,0.0007867888,0.001234163,0.0008799153,0.0004524296],"domain_scores_gemma":[0.9935613,0.0006236376,0.000715442,0.001705543,0.003085532,0.0003085514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003088286,0.001181028,0.02636112,0.001053349,0.001306086,0.0001456718,0.01031348,0.01042611,0.002771175,0.1059863,0.7879699,0.05245487],"study_design_scores_gemma":[0.001504921,0.000001547476,0.006185633,0.004421272,0.0001925876,0.0000325594,0.0003016295,0.6603433,0.004713816,0.001241788,0.3191791,0.00188186],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005984356,0.001346355,0.9340686,0.007493946,0.002458185,0.0005962878,0.0001114514,0.00046006,0.04748073],"genre_scores_gemma":[0.8401938,0.01122809,0.08516023,0.003633093,0.0004499743,0.0001396869,0.0128763,0.0002314884,0.04608734],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8489084,"threshold_uncertainty_score":0.9997368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007839695639777768,"score_gpt":0.2322741933442486,"score_spread":0.2244344977044708,"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."}}