{"id":"W2171719256","doi":"10.1007/s11284-011-0819-2","title":"Predicting the wetland distributions under climate warming in the Great Xing'an Mountains, northeastern China","year":2011,"lang":"en","type":"article","venue":"Ecological Research","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Key Technologies Research and Development Program; National Natural Science Foundation of China","keywords":"Wetland; Environmental science; Climate change; Global warming; Climatology; Evapotranspiration; Ecosystem; Physical geography; Precipitation; Ecology; Geography; Geology; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002110271,0.0003021833,0.0001765292,0.000317052,0.0003354943,0.0003726595,0.0003231033,0.0003288487,0.0005241143],"category_scores_gemma":[0.0003071378,0.0001712883,0.0004427234,0.0003765741,0.0001669679,0.0002700936,0.0001930728,0.00014944,0.00005925622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000611324,"about_ca_system_score_gemma":0.0007263729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09481415,"about_ca_topic_score_gemma":0.08342553,"domain_scores_codex":[0.9999461,0.00001053153,0.000003111228,0.00001611581,0.000008333379,0.00001576472],"domain_scores_gemma":[0.9998902,0.00002983972,0.00001907032,0.00001159481,0.00002714497,0.00002207088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001000538,0.0000835434,0.292022,0.00003710888,0.00008603078,0.0003073069,0.00009733505,0.6947519,0.002796741,0.0002297859,0.0005911537,0.008897021],"study_design_scores_gemma":[0.00002091946,0.00002624487,0.1407962,0.000006012981,0.00002574427,0.00002689896,0.0001099606,0.8579943,0.000515601,0.0001426347,0.0003244314,0.00001104667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998869,0.00002431672,0.0005271463,0.00003392123,0.000002739001,0.000004285489,0.0002537185,0.00003150403,0.000253327],"genre_scores_gemma":[0.9991032,0.00002008952,0.0004035015,0.000004709988,0.000002075012,0.000004579541,0.0003100742,0.000002474651,0.0001495328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09481415,"threshold_uncertainty_score":0.1885245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.089035058810259,"score_gpt":0.3267539920511394,"score_spread":0.2377189332408804,"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."}}