{"id":"W2352331294","doi":"","title":"Spatial heterogeneity analysis of soil seed bank for degraded grassland Stellera chamaejasme populations based on geostatistics","year":2015,"lang":"en","type":"article","venue":"Shengtaixue zazhi","topic":"Rangeland Management and Livestock Ecology","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Soil seed bank; Grassland; Geostatistics; Variogram; Grassland degradation; Vegetation (pathology); Spatial heterogeneity; Spatial distribution; Spatial variability; Environmental science; Soil science; Population; Ecology; Geography; Kriging; Agronomy; Biology; Remote sensing; Mathematics; Seedling","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":[0.0007859718,0.0001790869,0.0002819171,0.002170444,0.0003858471,0.0005207897,0.0002684818,0.0001832785,0.0007131053],"category_scores_gemma":[0.00201195,0.0001289475,0.000708758,0.001276352,0.0004210378,0.0003407999,0.0004081606,0.0001572816,0.00005341171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000504718,"about_ca_system_score_gemma":0.0004090632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01394012,"about_ca_topic_score_gemma":0.01428077,"domain_scores_codex":[0.9994994,0.0001495066,0.00004622711,0.0001525621,0.00009074856,0.00006140056],"domain_scores_gemma":[0.9987803,0.0005297737,0.0002894775,0.0001515496,0.0001454593,0.000103481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007098448,0.00002303603,0.9808601,0.00001958757,0.0002027646,0.0001281083,0.0002100563,0.008152666,0.001650939,0.0004197803,0.0001538861,0.008108119],"study_design_scores_gemma":[0.00000934777,0.00003835748,0.9088492,0.000006023718,0.00008207227,0.0001371875,0.0004230246,0.0892808,0.000411822,0.0005760219,0.0001692287,0.00001700795],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971129,0.00003143392,0.002556331,0.00001885534,0.000001305458,0.000003397,0.000119825,0.00002019748,0.000135778],"genre_scores_gemma":[0.999514,0.000006977781,0.0002957366,0.000001637797,0.00000107263,0.000002618375,0.000144466,0.000001330023,0.00003230323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01394012,"threshold_uncertainty_score":0.02771795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.043018066253003,"score_gpt":0.2722583194346647,"score_spread":0.2292402531816617,"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."}}