{"id":"W2283186049","doi":"10.1016/j.foreco.2016.01.013","title":"Scale effects in survey estimates of proportions and quantiles of per unit area attributes","year":2016,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Quantile; Statistics; Scale (ratio); Spatial analysis; Sampling (signal processing); Context (archaeology); Econometrics; Spatial ecology; Mathematics; Consistency (knowledge bases); Spatial distribution; Scaling; Forest inventory; Unit (ring theory); Environmental science; Geography; Computer science; Cartography; Ecology; Forestry; Forest management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07798628,0.0005444719,0.001042299,0.001167443,0.0007967903,0.002272986,0.001358415,0.001829399,0.004168286],"category_scores_gemma":[0.2713788,0.001140037,0.00201413,0.002242719,0.003498691,0.003227529,0.002629407,0.001917017,0.0005389279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008836389,"about_ca_system_score_gemma":0.0005064795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002556,"about_ca_topic_score_gemma":0.01369659,"domain_scores_codex":[0.9352781,0.04949281,0.001963312,0.007986569,0.00401297,0.001266217],"domain_scores_gemma":[0.4776668,0.4754578,0.009572715,0.03202417,0.004708079,0.0005704806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002097624,0.0001619389,0.7267953,0.0006332824,0.004268588,0.000499382,0.002544502,0.125319,0.00666662,0.0365883,0.004860736,0.08956489],"study_design_scores_gemma":[0.0001102475,0.0002744435,0.8376299,0.0001106132,0.00114103,0.0004168894,0.0009797144,0.1150069,0.003581797,0.03735366,0.0032669,0.0001278705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5694243,0.002175921,0.4122308,0.00133707,0.0001982233,0.0002869276,0.001913335,0.001098468,0.01133495],"genre_scores_gemma":[0.9852284,0.0001330775,0.01255273,0.000284535,0.00004856505,0.0000658337,0.0004940797,0.000114057,0.001078709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07798628,"threshold_uncertainty_score":0.4124358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460960754409362,"score_gpt":0.241163517073277,"score_spread":0.2265539095291833,"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."}}