{"id":"W4406882419","doi":"10.3389/ffgc.2024.1457522","title":"Bark water affects the isotopic composition of xylem water in tropical rainforest trees","year":2025,"lang":"en","type":"article","venue":"Frontiers in Forests and Global Change","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Department of Environment and Science, Queensland Government; Australian Government; Global Institute for Water Security, University of Saskatchewan","keywords":"Tropical rainforest; Rainforest; Bark (sound); Xylem; Environmental science; Tropics; Subtropics; Agroforestry; Forestry; Ecology; Geography; Botany; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007995163,0.00008293123,0.0001228781,0.00003962429,0.00003971632,0.0000157966,0.000107218,0.00006526755,0.00001956642],"category_scores_gemma":[0.000001304152,0.00004333897,0.00002590522,0.0001022665,0.0001276547,0.00009547103,0.00008954408,0.00006664937,0.000003308824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009261673,"about_ca_system_score_gemma":0.000001455317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004783679,"about_ca_topic_score_gemma":0.004029779,"domain_scores_codex":[0.9994233,0.00003392078,0.0001236687,0.000128495,0.00008895836,0.0002016263],"domain_scores_gemma":[0.9998596,0.000006190273,0.00001323522,0.00009610643,0.000001814806,0.00002301135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000302746,0.00002435785,0.9964973,0.00000971688,0.000004671603,0.000005753455,0.0004881853,0.0002866835,0.00005609417,0.000526628,0.0005478853,0.001522434],"study_design_scores_gemma":[0.000412794,0.00003134371,0.9804109,0.00003036386,0.0000070093,0.000002775208,0.00003443495,0.01332007,0.000158983,0.003962056,0.001565081,0.00006414903],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996549,0.0001019748,0.0003687367,0.0006170403,0.0001921297,0.0002454008,0.00001439182,0.000005657104,0.001905697],"genre_scores_gemma":[0.9995093,0.00004402021,0.0001266953,0.0001366388,0.00001137402,0.00002488067,0.00002795015,0.000002080676,0.0001170121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01608638,"threshold_uncertainty_score":0.2248711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005685131008400113,"score_gpt":0.203121908980572,"score_spread":0.1974367779721719,"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."}}