{"id":"W2043835521","doi":"10.3138/carto.49.3.2142","title":"Spreading Map Information over Different Depth Layers – An Improvement for Map-Reading Efficiency?","year":2014,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kwame Nkrumah University of Science and Technology; Deutsche Forschungsgemeinschaft","keywords":"Thematic map; Stereoscopy; Reading (process); Visualization; Depth map; Computer science; Geography; Cartography; Computer graphics (images); Information retrieval; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008359798,0.0002267083,0.0001618904,0.0008106405,0.0005500815,0.0008396413,0.0002775765,0.0001220643,0.00002023897],"category_scores_gemma":[0.0001231027,0.0001816163,0.0001806942,0.0002306905,0.00006232021,0.002625048,0.00002813554,0.0001674827,0.000004311134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006133233,"about_ca_system_score_gemma":0.00001735875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001947481,"about_ca_topic_score_gemma":0.00003487437,"domain_scores_codex":[0.9983675,0.00003602165,0.0007184997,0.0001083276,0.0004970707,0.0002725139],"domain_scores_gemma":[0.9984918,0.000139808,0.0003153929,0.0001283782,0.0007865435,0.000138075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001136453,0.0002448331,0.02279744,0.001124371,0.001227908,5.952459e-7,0.01122339,0.07150798,0.00416592,0.1997347,0.01184051,0.6749959],"study_design_scores_gemma":[0.003222507,0.0004926905,0.006405927,0.0001423033,0.0001210528,0.00002307895,0.001162839,0.827455,0.002070411,0.007735044,0.150678,0.0004910557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2533325,0.00004229598,0.7415965,0.0005121527,0.003140157,0.0008822089,0.00009173151,0.0001898064,0.0002126739],"genre_scores_gemma":[0.9963072,0.0001836196,0.0003095343,0.00114487,0.0003462436,0.0001639753,0.001517989,0.00001841585,0.000008140044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7559471,"threshold_uncertainty_score":0.809668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007046306025354187,"score_gpt":0.254390372804357,"score_spread":0.2473440667790028,"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."}}