{"id":"W2031861178","doi":"10.1111/j.1365-2699.2005.01353.x","title":"Simulating forest ecosystem response to climate warming incorporating spatial effects in north‐eastern China","year":2005,"lang":"en","type":"article","venue":"Journal of Biogeography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Sciences","keywords":"Environmental science; Disturbance (geology); Climate change; Global warming; Forest ecology; Seed dispersal; Ecosystem; Biological dispersal; Spatial ecology; Common spatial pattern; Precipitation; Ecology; Climate model; Climatology; Geography; Meteorology; Geology; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009517114,0.000158826,0.0002507089,0.0002894016,0.000123844,0.00006303717,0.0002301905,0.00005607259,0.0004713473],"category_scores_gemma":[0.0001307634,0.0001384809,0.0001756827,0.0008052668,0.0000357093,0.0002851867,0.0001547944,0.0001803341,0.0001495855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926679,"about_ca_system_score_gemma":0.000007872816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001507665,"about_ca_topic_score_gemma":0.00883882,"domain_scores_codex":[0.9983205,0.0001517839,0.0005976264,0.0001739692,0.0004039805,0.0003521971],"domain_scores_gemma":[0.9990799,0.0001209491,0.0004413998,0.0001365553,0.00002118235,0.0001999502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003381612,0.00008433474,0.9838388,0.00002109916,0.000008220753,0.00005251732,0.000385683,0.005504861,0.003267139,0.000006805142,0.00004012909,0.006452277],"study_design_scores_gemma":[0.0007879602,0.0003011802,0.9934779,0.0001233371,0.0000113841,0.0000315464,0.0003348103,0.002660195,0.0004758522,0.00001080713,0.001624345,0.0001606645],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983903,0.00004703319,0.0002913775,0.0003462236,0.0001812132,0.0001859183,0.00002946318,0.0000153326,0.0005131952],"genre_scores_gemma":[0.9990667,0.00001064528,0.0006099751,0.0001664398,0.0001213911,0.000004216574,0.000004869928,0.00001296326,0.000002845127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009639146,"threshold_uncertainty_score":0.5647088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008840805951683897,"score_gpt":0.2318002236695914,"score_spread":0.2229594177179075,"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."}}