{"id":"W4406133577","doi":"10.1002/met.70023","title":"Estimating latent heat flux of subtropical forests using machine learning algorithms","year":2025,"lang":"en","type":"article","venue":"Meteorological Applications","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Latent heat; Subtropics; Flux (metallurgy); Computer science; Heat flux; Algorithm; Machine learning; Meteorology; Artificial intelligence; Environmental science; Geography; Heat transfer; Physics; Materials science","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":[0.0001513875,0.00009900314,0.000159774,0.00003764037,0.0001789103,0.00001370122,0.0001792261,0.00008692376,0.0002578043],"category_scores_gemma":[0.00002998117,0.00007782611,0.00006117104,0.0003403481,0.0001443083,0.0000456772,0.0001885921,0.0001806907,0.00003645954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006688002,"about_ca_system_score_gemma":0.000006049571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002115424,"about_ca_topic_score_gemma":0.0000513267,"domain_scores_codex":[0.999131,0.00005170502,0.0002628846,0.0002399004,0.0001339354,0.000180577],"domain_scores_gemma":[0.9996343,0.00008668409,0.00005171836,0.0001673895,0.000009598103,0.00005036332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001087227,0.0001574408,0.4999987,0.00001094263,0.00002237512,0.000001854272,0.00002423545,0.4590413,0.02422637,0.005095952,0.00001392724,0.011396],"study_design_scores_gemma":[0.0001248873,0.00003166433,0.05168106,0.000005339873,0.00003048142,0.000005502885,0.000001825231,0.9407377,0.0005460319,0.005376039,0.001382362,0.00007708903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6105419,0.00003811487,0.3875169,0.0001330058,0.00002134125,0.0002054177,0.00001061566,0.00004522354,0.00148745],"genre_scores_gemma":[0.9106374,0.000003900512,0.08885361,0.00005351101,0.00001184906,0.0000694413,0.00004873265,0.000004688008,0.0003168237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4816964,"threshold_uncertainty_score":0.3173657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540540206382466,"score_gpt":0.2515420585237566,"score_spread":0.236136656459932,"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."}}