{"id":"W2126532874","doi":"10.1034/j.1600-0587.2002.250510.x","title":"A balanced view of scale in spatial statistical analysis","year":2002,"lang":"en","type":"article","venue":"Ecography","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":705,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Polytechnique Montréal; University of Alberta","funders":"","keywords":"Scale (ratio); Statistics; Spatial analysis; Range (aeronautics); Autocorrelation; Spatial ecology; Ecology; Temporal scales; Variance (accounting); Variogram; Sample size determination; Sampling (signal processing); Population; Geography; Mathematics; Computer science; Cartography; Kriging; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009001089,0.00005942898,0.000156396,0.0001178658,0.00001946724,0.000006424324,0.00008058879,0.00002212699,0.006274213],"category_scores_gemma":[0.00001598738,0.00005760922,0.00006232339,0.0009672065,0.0000995345,0.00002767273,0.00003741945,0.00004538158,0.00008644971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000103445,"about_ca_system_score_gemma":7.591674e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254425,"about_ca_topic_score_gemma":0.002413457,"domain_scores_codex":[0.9993473,0.000026121,0.0001766067,0.0001593792,0.0001426464,0.0001479931],"domain_scores_gemma":[0.999738,0.00004442821,0.00004251329,0.0001237109,0.000003142008,0.00004818026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002333645,0.00007056997,0.9532944,0.000005881517,0.00002105457,0.000003136859,0.0001696413,0.0002782372,0.000097818,0.00006968935,0.0006275146,0.04535975],"study_design_scores_gemma":[0.0001484735,0.00002490892,0.9805901,0.000004305117,0.00004276558,2.605962e-7,0.00001653008,0.01700304,0.00003046735,0.0006654621,0.001400915,0.00007275147],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9323389,0.00009980225,0.04405759,0.00006744164,0.0000612623,0.0001197039,0.00009021966,0.00001584238,0.02314925],"genre_scores_gemma":[0.9942108,0.00005218363,0.005631994,0.00005259197,0.000005880986,0.000006375834,0.000008798375,0.000003330476,0.00002804877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06187191,"threshold_uncertainty_score":0.9946342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007611264277835186,"score_gpt":0.2122003492496362,"score_spread":0.204589084971801,"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."}}