{"id":"W3096216788","doi":"10.1016/j.ecolind.2020.107118","title":"Water table drawdown increases plant biodiversity and soil polyphenol in the Zoige Plateau","year":2020,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Sichuan Province Science and Technology Support Program; South University of Science and Technology of China; National Natural Science Foundation of China; Yale University","keywords":"Drawdown (hydrology); Water table; Peat; Environmental science; Species richness; Vegetation (pathology); Biomass (ecology); Biodiversity; Chemistry; Environmental chemistry; Hydrology (agriculture); Agronomy; Ecology; Groundwater; Geology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000250351,0.0001190074,0.0001700328,0.00003056266,0.0002213274,0.0000321519,0.0003017214,0.000128027,0.004202086],"category_scores_gemma":[0.00004797888,0.00006016289,0.00002709009,0.0001753756,0.0002850511,0.00008343875,0.0003689709,0.0002059023,0.0005485648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004086245,"about_ca_system_score_gemma":0.000006234825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006975187,"about_ca_topic_score_gemma":0.0005979298,"domain_scores_codex":[0.9989454,0.0001129235,0.0001436151,0.0003028258,0.0001325357,0.0003627634],"domain_scores_gemma":[0.9996055,0.0001108823,0.00003688099,0.00009263856,9.143892e-7,0.0001531666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003198975,0.0001302921,0.9889537,0.000001980731,0.00000572211,0.0001260413,0.0009521042,0.00002344547,0.0003207047,0.00003544051,0.009275395,0.0001431956],"study_design_scores_gemma":[0.0004537272,0.0002692175,0.9812086,9.348745e-7,0.00001023907,0.00001454821,0.0002541051,0.00008258108,0.0004186499,0.0001760091,0.01697741,0.000133963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927498,0.00001381969,0.000002236345,0.003758624,0.00002394796,0.0001549157,0.00003582289,0.00002861065,0.003232258],"genre_scores_gemma":[0.9950222,0.00003023538,0.00001618347,0.004826277,0.00002568376,0.00001257756,0.00004062338,0.000002302335,0.00002385895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00774507,"threshold_uncertainty_score":0.9967082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196392192629978,"score_gpt":0.1896160322417156,"score_spread":0.1776521103154158,"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."}}