{"id":"W4301525309","doi":"10.1007/978-3-031-02598-3_8","title":"Decision 4: How to Spatially Arrange the Visual Levels, Embedded or Separate?","year":2011,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on visualization","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Perception; Embedding; Space (punctuation); Graph; Distortion (music); Human–computer interaction; Artificial intelligence; Theoretical computer science; Psychology","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002505755,0.0006189733,0.000470977,0.0004184372,0.0002201211,0.0001898903,0.0003075034,0.0005703849,0.003084962],"category_scores_gemma":[0.0004882423,0.0004402717,0.0001816094,0.0001784323,0.00004533934,0.0001214146,0.00003830353,0.0003160535,0.0007184769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001753614,"about_ca_system_score_gemma":0.00006469458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001127088,"about_ca_topic_score_gemma":0.0003873272,"domain_scores_codex":[0.9980288,0.00008019568,0.0004387568,0.0004856386,0.0006572565,0.0003093593],"domain_scores_gemma":[0.9984743,0.0005479968,0.0001751335,0.0004251705,0.0002308511,0.0001465692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002313545,0.0001803852,0.000007685368,0.0004839568,0.0009325654,0.00005826586,0.004141068,0.01176122,0.006969132,0.05239983,0.0855112,0.8352411],"study_design_scores_gemma":[0.001605015,0.001840623,0.0008091775,0.005545534,0.001206732,0.00004254034,0.00009255342,0.03555363,0.2653632,0.02422568,0.6584368,0.005278461],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003217947,0.0007172453,0.6308313,0.000429205,0.004013176,0.00426099,0.0004374313,0.002327517,0.3537652],"genre_scores_gemma":[0.9562934,0.0004450173,0.0004003746,0.001402676,0.001243822,0.0002607767,0.0003574137,0.0005351075,0.0390614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9530755,"threshold_uncertainty_score":0.9998049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04202207923029103,"score_gpt":0.2851398981018217,"score_spread":0.2431178188715307,"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."}}