{"id":"W4221077097","doi":"10.1038/s41561-022-00911-8","title":"Tropical tree growth driven by dry-season climate variability","year":2022,"lang":"en","type":"article","venue":"Nature Geoscience","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Pantropical; Environmental science; Tropical vegetation; Dry season; Carbon sink; Tropical and subtropical dry broadleaf forests; Wet season; Tropical savanna climate; Tropics; Precipitation; Climatology; Climate change; Biomass partitioning; Vegetation (pathology); Productivity; Biomass (ecology); Sink (geography); Tropical climate; Atmospheric sciences; Ecology; Agroforestry; Geography; Biology; Ecosystem","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":[],"consensus_categories":[],"category_scores_codex":[0.0004028268,0.0001187542,0.0001089291,0.00002626181,0.000542959,0.00004053752,0.0006136987,0.0001008068,0.0009054476],"category_scores_gemma":[0.00007055175,0.0001051054,0.00005463848,0.0005511973,0.0002080356,0.0001943047,0.0006127006,0.0006881581,0.00005908501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002777003,"about_ca_system_score_gemma":0.00001586415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009095506,"about_ca_topic_score_gemma":0.00004911372,"domain_scores_codex":[0.9981515,0.0001292085,0.0001465868,0.0004978254,0.0006771604,0.000397756],"domain_scores_gemma":[0.9994885,0.00005720751,0.00005978638,0.0002699058,0.000006510295,0.0001180807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001601696,0.0001412152,0.9888114,0.000002814303,0.000002079859,0.00001397165,0.00009587139,0.00139156,0.003393459,0.003449566,0.001544849,0.001137182],"study_design_scores_gemma":[0.0003562233,0.0001401925,0.7887582,0.000002906444,0.00001615446,0.00003961225,0.00002790478,0.1682335,0.0001007493,0.00310619,0.03883283,0.0003855465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862443,0.00002722069,0.001370284,0.0007581988,0.0003678948,0.0001617813,0.0002297717,0.00006720546,0.01077335],"genre_scores_gemma":[0.9972863,0.00001378832,0.001410457,0.0005289448,0.00001607209,0.00002712932,0.00004213881,0.000006542906,0.0006686055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2000533,"threshold_uncertainty_score":0.9914016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002426460641285764,"score_gpt":0.1900805555793439,"score_spread":0.1876540949380582,"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."}}