{"id":"W2128499319","doi":"10.1080/10824000009480529","title":"Multiscale Analysis of Landscape Heterogeneity: Scale Variance and Pattern Metrics","year":2000,"lang":"en","type":"article","venue":"Geographic information sciences","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":313,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"U.S. Department of Agriculture","keywords":"Semivariance; Landscape ecology; Spatial ecology; Scale (ratio); Ecology; Variance (accounting); Spatial heterogeneity; Computer science; Spatial variability; Geography; Econometrics; Statistics; Cartography; Mathematics; Biology","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":[0.0005155907,0.00007326325,0.0001500925,0.0003267598,0.0001950087,0.00008641791,0.0002066301,0.00003802725,0.001718153],"category_scores_gemma":[0.000004628121,0.00005296457,0.00006067497,0.002856866,0.00009107416,0.001247886,0.0000527841,0.00002940097,0.0001153941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004178979,"about_ca_system_score_gemma":0.000003191073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001126701,"about_ca_topic_score_gemma":0.001776541,"domain_scores_codex":[0.9990386,0.0000232341,0.0002765221,0.0001300351,0.0003663817,0.0001652012],"domain_scores_gemma":[0.9996498,0.00003235808,0.0001192704,0.0001291445,0.00001149721,0.00005792476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001866263,0.000008786628,0.9504956,0.000007880021,0.0000254372,8.067942e-8,0.000374159,0.006047844,0.000004663256,0.000002066355,0.00001755067,0.04301409],"study_design_scores_gemma":[0.0001075344,0.00002920507,0.8391948,0.000004569,0.00006613639,0.000001544007,0.0001110212,0.1585877,0.0000588886,0.00002050592,0.001739449,0.0000785489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925292,0.00005151495,0.0005152393,0.00005258966,0.00003101139,0.00006893765,0.00002522579,0.00001644807,0.006709903],"genre_scores_gemma":[0.9993539,0.0001708722,0.0002492521,0.0001945637,0.000004612549,0.000004882747,0.00001244464,9.796992e-7,0.000008492264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1525399,"threshold_uncertainty_score":0.9991944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007654953682362398,"score_gpt":0.2161247483175092,"score_spread":0.2084697946351468,"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."}}