{"id":"W2052795242","doi":"10.1007/s10980-010-9474-1","title":"Quantifying historic landscape heterogeneity from aerial photographs using object-based analysis","year":2010,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Forests, Lands and Natural Resource Operations","keywords":"Spatial heterogeneity; Landscape ecology; Geography; Context (archaeology); Ecology; Spatial ecology; Cartography; Landscape epidemiology; Baseline (sea); Spatial analysis; Terrain; Remote sensing; Habitat; Geology; Biology; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006824219,0.0003072233,0.0005058851,0.00787201,0.0003396112,0.0009958555,0.000440655,0.0003366105,0.001272965],"category_scores_gemma":[0.001346512,0.000375298,0.0003690323,0.004702463,0.000300218,0.001286837,0.0005162359,0.0001931474,0.0003365032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006906244,"about_ca_system_score_gemma":0.000251459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01281263,"about_ca_topic_score_gemma":0.04045226,"domain_scores_codex":[0.9996268,0.00004776168,0.00003212732,0.0001064817,0.0001402141,0.00004660587],"domain_scores_gemma":[0.998843,0.0003566225,0.000252682,0.0002175714,0.0002774153,0.00005277575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002212728,0.000182622,0.6105341,0.0003466262,0.0005332768,0.0004565392,0.0005796968,0.04272217,0.02349787,0.000996122,0.0017999,0.3181298],"study_design_scores_gemma":[0.00001331463,0.00005126221,0.9039518,0.00004000324,0.0002224486,0.000580664,0.0004936437,0.08880763,0.002988827,0.0008364624,0.001973857,0.00004011032],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.939672,0.0006543606,0.05148064,0.0000320236,0.00001742251,0.000106864,0.003036586,0.0004884664,0.004511677],"genre_scores_gemma":[0.972151,0.0002035724,0.02515383,0.00000899285,0.0000113227,0.0000307094,0.002085937,0.00003906882,0.0003155271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01281263,"threshold_uncertainty_score":0.0254761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731209364155028,"score_gpt":0.2444598345853187,"score_spread":0.2271477409437684,"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."}}