{"id":"W4230227854","doi":"10.1002/9781118786352.wbieg1015","title":"Map Generalization","year":2017,"lang":"en","type":"other","venue":"International Encyclopedia of Geography","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"InterDigital (Canada)","funders":"","keywords":"Cartographic generalization; Generalization; Computer science; Abstraction; Automation; Selection (genetic algorithm); Geographic information system; Data science; Focus (optics); Process (computing); Range (aeronautics); Data exploration; Information retrieval; Geography; Data mining; Artificial intelligence; Cartography; Mathematics; Visualization; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002667949,0.001228967,0.0008999698,0.003227911,0.001858166,0.005302868,0.003759965,0.001356685,0.1033503],"category_scores_gemma":[0.01409727,0.00063228,0.001608839,0.004927757,0.001422446,0.007692145,0.007661323,0.001894186,0.03935258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477169,"about_ca_system_score_gemma":0.002742289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007012147,"about_ca_topic_score_gemma":0.007287235,"domain_scores_codex":[0.9971022,0.0005627508,0.0001864648,0.0006914366,0.001230822,0.0002262797],"domain_scores_gemma":[0.9949825,0.0008847471,0.0001988414,0.002253247,0.001459282,0.0002214444],"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":[0.0001035496,0.00006117321,0.001851681,0.0006114268,0.0000763918,0.0003963098,0.001813664,0.01172913,0.001669482,0.2235914,0.2755638,0.482532],"study_design_scores_gemma":[0.00001546778,0.0000226699,0.00073329,0.0001473208,0.00002633815,0.0003815836,0.0005526787,0.009395657,0.001383945,0.07805342,0.9092492,0.00003837821],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.006570016,0.001116507,0.5632336,0.002224373,0.0009635592,0.0008008564,0.008617124,0.02242016,0.3940539],"genre_scores_gemma":[0.1827355,0.004210139,0.5409206,0.001881469,0.00063759,0.001345265,0.02791167,0.009859095,0.2304986],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1033503,"threshold_uncertainty_score":0.3457412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01082821349734014,"score_gpt":0.2883441278188444,"score_spread":0.2775159143215042,"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."}}