{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001922546,0.0001598212,0.0001985223,0.000214923,0.0002044919,0.0004280128,0.0002176355,0.0002042541,0.002549504],"category_scores_gemma":[0.0006661637,0.0001616338,0.0002699194,0.000334907,0.0002525132,0.0002977466,0.000275874,0.0002745706,0.0001868014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004526892,"about_ca_system_score_gemma":0.0003194797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01446718,"about_ca_topic_score_gemma":0.01745152,"domain_scores_codex":[0.9999474,0.000008605443,0.000002259284,0.00001668804,0.000004651293,0.00002032795],"domain_scores_gemma":[0.9997157,0.00008322506,0.00005907436,0.00002314842,0.00003892768,0.00007979752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001031586,0.0001733638,0.5871329,0.0001771744,0.0003673102,0.001031262,0.0004485139,0.2664224,0.1149487,0.009771587,0.005153984,0.0133412],"study_design_scores_gemma":[0.00004929948,0.00003828661,0.7234175,0.000008742651,0.00005583359,0.0001608568,0.0001588959,0.2715383,0.002113648,0.001626365,0.0008046147,0.00002751439],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973991,0.00005372534,0.0006982553,0.0001177916,0.000009873796,0.000002311259,0.0005029723,0.0000417039,0.001174283],"genre_scores_gemma":[0.9995338,0.00002399073,0.00006622183,0.00001031073,0.000003976687,0.000001042574,0.0001625104,0.00001194147,0.0001862183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01446718,"threshold_uncertainty_score":0.02876592,"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."}}