{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008929406,0.000575848,0.0004201509,0.0009734691,0.0002917389,0.0006559839,0.0005582814,0.0005464499,0.0006103864],"category_scores_gemma":[0.002031599,0.0002302278,0.0005148731,0.0007282721,0.0002602415,0.00055993,0.0003358198,0.0004694434,0.0002011223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005130762,"about_ca_system_score_gemma":0.0005919235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017283,"about_ca_topic_score_gemma":0.007635223,"domain_scores_codex":[0.9998272,0.00006209577,0.00001061352,0.00004618929,0.00003217013,0.00002174538],"domain_scores_gemma":[0.9992092,0.0005404589,0.00007289428,0.00003996483,0.0001121034,0.00002542993],"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":[0.00004567756,0.00005115942,0.008247791,0.00002338586,0.00003394552,0.00002453039,0.00001335198,0.9556518,0.00120841,0.0004532942,0.0002415582,0.03400517],"study_design_scores_gemma":[8.754896e-7,0.000001768743,0.000539221,0.000001095725,8.741589e-7,0.000001383666,0.000001971415,0.9991135,0.0001119402,0.0002059331,0.000020557,9.690435e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6238044,0.0005528906,0.3720618,0.0002022487,0.00003864021,0.00005113882,0.0005490111,0.0009720204,0.001767756],"genre_scores_gemma":[0.9656393,0.00006840436,0.03343998,0.00002215199,0.00001860607,0.00003478881,0.0003698735,0.00001879863,0.000388058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01017283,"threshold_uncertainty_score":0.02022719,"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."}}