{"id":"W1992210522","doi":"10.3138/carto.49.1.2137","title":"A Comprehensive Multi-criteria Model for High Cartographic Quality Point-Feature Label Placement","year":2014,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universität Heidelberg; Deutscher Akademischer Austauschdienst","keywords":"Legibility; Computer science; Feature (linguistics); Point (geometry); Process (computing); Lettering; Quality (philosophy); Artificial intelligence; Annotation; Data mining; Information retrieval; Engineering drawing; Engineering; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005549778,0.001336595,0.001486458,0.002263583,0.0009892526,0.003972772,0.002818182,0.002540973,0.005440769],"category_scores_gemma":[0.01149752,0.000750981,0.001334769,0.00251855,0.001757245,0.003079251,0.002115757,0.001582719,0.001003391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003390589,"about_ca_system_score_gemma":0.001789362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009030677,"about_ca_topic_score_gemma":0.005872257,"domain_scores_codex":[0.9959517,0.001788514,0.0001964361,0.0007090031,0.001022571,0.0003317499],"domain_scores_gemma":[0.9943281,0.003434392,0.0005352486,0.0003477934,0.001102856,0.0002515593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000463329,0.00005967781,0.0009878175,0.0001260884,0.00004661629,0.0001432125,0.0002003085,0.9377249,0.0009116911,0.03732208,0.001231391,0.02119995],"study_design_scores_gemma":[0.000009962477,0.00005058278,0.0003673539,0.00002233229,0.00001458207,0.00004239546,0.00003753512,0.9802989,0.0002165335,0.01811311,0.0008086925,0.00001800392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01707144,0.0001782344,0.976485,0.0004096213,0.00002028938,0.0001430986,0.0001974786,0.0001792735,0.005315597],"genre_scores_gemma":[0.5894946,0.0003142704,0.4003402,0.0001429875,0.0000520492,0.0007180517,0.0003955963,0.0001348925,0.008407333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009030677,"threshold_uncertainty_score":0.02935034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04584375920633951,"score_gpt":0.3727841811951622,"score_spread":0.3269404219888227,"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."}}