{"id":"W2919690626","doi":"10.1016/j.agrformet.2019.02.031","title":"Growth rate rather than growing season length determines wood biomass in dry environments","year":2019,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; National Science Foundation","keywords":"Xylem; Growing season; Biomass (ecology); Environmental science; Arid; Carbon sequestration; Plateau (mathematics); Ecosystem; Precipitation; Dry season; Agronomy; Ecology; Carbon dioxide; Biology; Botany; Geography","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":[0.0002309119,0.0001542542,0.0003078669,0.0003030413,0.0002480612,0.00044849,0.0001962333,0.0002125211,0.001406202],"category_scores_gemma":[0.0006780822,0.0002975931,0.0001719517,0.0002247131,0.000314915,0.0005717986,0.0003139742,0.0002657946,0.0003539154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001249246,"about_ca_system_score_gemma":0.0001445168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001891974,"about_ca_topic_score_gemma":0.00671407,"domain_scores_codex":[0.9999584,0.000009750104,0.000002803257,0.00001113646,0.000004087527,0.0000138231],"domain_scores_gemma":[0.9991098,0.0003504484,0.0002083996,0.00005154554,0.00004783176,0.000231898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001183847,0.0001098027,0.7305574,0.0001029016,0.000114222,0.0002090669,0.0003376493,0.001466642,0.2560853,0.0003054318,0.000222061,0.009305687],"study_design_scores_gemma":[0.000005885592,0.00004838322,0.9970422,0.000002873942,0.00001453163,0.00008011297,0.00009876144,0.0007645403,0.001645271,0.0001806063,0.0001125705,0.000004205024],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992098,0.00007877315,0.000304425,0.00001206893,0.000002471499,0.000001682018,0.00008055304,0.000006498196,0.0003037895],"genre_scores_gemma":[0.9989422,0.00008049276,0.0002104981,0.00002334356,0.00000524575,0.000003743434,0.0002265812,0.00002488748,0.0004830777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001891974,"threshold_uncertainty_score":0.004704177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003952784194070818,"score_gpt":0.1680226591942403,"score_spread":0.1640698750001695,"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."}}