{"id":"W2121435312","doi":"10.1111/j.1475-4762.2010.00956.x","title":"Urbanisation viewed through a geostatistical lens applied to remote-sensing data","year":2010,"lang":"en","type":"article","venue":"Area","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Variogram; Land cover; Urbanization; Change detection; Land use; Feature (linguistics); Geography; Variance (accounting); Geostatistics; Cover (algebra); Environmental science; Cartography; Physical geography; Computer science; Kriging; Spatial variability; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001456114,0.0000783966,0.00009385207,0.000007257196,0.00009746802,0.00003570358,0.0002428792,0.00004456543,0.001125308],"category_scores_gemma":[0.00003191612,0.0000619561,0.000009643097,0.0000868116,0.00001138008,0.000194657,0.0002704792,0.00007888609,0.002618095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001744969,"about_ca_system_score_gemma":0.000004569508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00219153,"about_ca_topic_score_gemma":0.009301834,"domain_scores_codex":[0.9992321,0.00001054542,0.0001244421,0.00029382,0.0001591223,0.0001800128],"domain_scores_gemma":[0.9992961,0.00004739031,0.00003035258,0.0005617592,0.00000351153,0.000060841],"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":[0.000261494,0.0002780465,0.03068014,0.0001807239,0.0001073173,0.000132484,0.0117196,0.001023891,0.2510395,0.006747167,0.1330995,0.5647302],"study_design_scores_gemma":[0.001096484,0.0001165976,0.1406139,0.00007131533,0.0001114341,0.00007068303,0.0003656025,0.1850786,0.005291104,0.01427873,0.651819,0.001086481],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409859,0.000003794963,0.01918915,0.0007300025,0.0002637765,0.0002265234,0.00004337202,0.0000606863,0.03849677],"genre_scores_gemma":[0.971635,0.000003249186,0.02715923,0.0009989409,0.00007369369,4.933614e-7,0.00009036246,0.000009164856,0.00002981738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5636437,"threshold_uncertainty_score":0.9997878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04224343609986375,"score_gpt":0.2603391318081489,"score_spread":0.2180956957082851,"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."}}