{"id":"W1976848127","doi":"10.3138/carto.45.1.47","title":"Using Spatial Analysis and Geovisualization to Reveal Urban Changes: Milan, Italy, 1737–2005","year":2010,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Parallels; Geography; Regional science; Geovisualization; City region; Cartography; Modernization theory; Urban structure; Politics; Economic geography; 3D city models; Scale (ratio); Urban planning; Civil engineering; Political science; Engineering; Computer science; Visualization; Data mining; Law","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":[0.0004262418,0.0001970426,0.0001236541,0.003037276,0.0005183512,0.001198112,0.0002454797,0.0002059836,0.001733746],"category_scores_gemma":[0.002200278,0.0001339558,0.0001364433,0.00565725,0.0008165602,0.000583794,0.0006163186,0.0001986246,0.0002531957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00279542,"about_ca_system_score_gemma":0.0006547525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09023303,"about_ca_topic_score_gemma":0.2846994,"domain_scores_codex":[0.9997739,0.00006772146,0.00001431839,0.00004016687,0.00006185799,0.00004209134],"domain_scores_gemma":[0.999388,0.000200134,0.000184275,0.00006722971,0.0001211277,0.00003921489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003248624,0.00006252962,0.5448614,0.0006087896,0.0001535694,0.001610392,0.09837106,0.004574894,0.001658714,0.01078039,0.05118655,0.2858069],"study_design_scores_gemma":[0.000005044422,0.00002024764,0.9386896,0.00006853309,0.00003619709,0.0002300602,0.01115433,0.001009888,0.0003468413,0.0003950225,0.04802368,0.00002060168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9366022,0.002619973,0.001890113,0.001364738,0.00007407153,0.00003632641,0.003044074,0.0001676618,0.05420087],"genre_scores_gemma":[0.9937183,0.0007246009,0.001655765,0.0000388042,0.00005323506,0.00002605963,0.001118788,0.00002441322,0.002640185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09023303,"threshold_uncertainty_score":0.1794156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033997302698559,"score_gpt":0.2732090354488915,"score_spread":0.2628690624219059,"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."}}