{"id":"W4412769844","doi":"10.1016/j.agrformet.2025.110759","title":"Land surface temperatures outperform gridded air temperatures in modeling forest growth across the Northern Hemisphere","year":2025,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Key Research and Development Program of Zhejiang Province; Zhejiang University; National Natural Science Foundation of China","keywords":"Environmental science; Northern Hemisphere; Climatology; Atmospheric sciences; Biometeorology; Southern Hemisphere; Meteorology; Geology; Geography; Canopy","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":[0.001483505,0.0007439428,0.0005224093,0.0002577743,0.0005462033,0.001185481,0.0005661006,0.0009879161,0.001462043],"category_scores_gemma":[0.002997957,0.0003645034,0.0005421413,0.0004263367,0.000499525,0.001348583,0.0004146876,0.0005953594,0.0001718061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364999,"about_ca_system_score_gemma":0.001369758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2126543,"about_ca_topic_score_gemma":0.1828052,"domain_scores_codex":[0.9997677,0.00009330561,0.00001270502,0.00006828669,0.00001514557,0.00004285973],"domain_scores_gemma":[0.9992198,0.0004893623,0.00005836667,0.00005664353,0.00008722848,0.00008859368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002843176,0.000145866,0.07744136,0.0000469147,0.0001160623,0.00007991381,0.00009009887,0.9140443,0.001348478,0.0002759572,0.0006356759,0.005490988],"study_design_scores_gemma":[0.00008797536,0.00009641043,0.04043548,0.000009030111,0.00005198289,0.00001696003,0.0002012855,0.9575875,0.0006738261,0.000421774,0.0004021129,0.00001567687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977291,0.0001401359,0.0005284566,0.0001916806,0.00002218321,0.000006929324,0.0003464823,0.00009457836,0.0009403905],"genre_scores_gemma":[0.9989775,0.0000633781,0.0003597965,0.00002734033,0.000008249844,0.000004744513,0.0002061305,0.00001487483,0.0003379943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2126543,"threshold_uncertainty_score":0.422833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007734942021371636,"score_gpt":0.213448312014122,"score_spread":0.2057133699927504,"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."}}