{"id":"W4415815798","doi":"10.3390/rs17213630","title":"Integrating Remotely Sensed Thermal Observations for Calibration of Process-Based Land-Surface Models: Accuracy, Revisit Windows, and Implications in a Dryland Ecosystem","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Space Agency; Narodowa Agencja Wymiany Akademickiej; Danish Data Science Academy","keywords":"Eddy covariance; Moderate-resolution imaging spectroradiometer; Evapotranspiration; Satellite; Sensible heat; Latent heat; Thermal infrared; Temporal resolution; Ecosystem","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002993628,0.0005148413,0.0003612913,0.0004286624,0.0002468193,0.001044593,0.0006211426,0.0005793303,0.0002288891],"category_scores_gemma":[0.005658609,0.0002878477,0.0005038269,0.0008403875,0.000250444,0.000711629,0.0004249778,0.000393506,0.0001085151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009201623,"about_ca_system_score_gemma":0.0005234418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02851013,"about_ca_topic_score_gemma":0.02341741,"domain_scores_codex":[0.9994537,0.0001636981,0.0000634653,0.0001512281,0.0001265699,0.00004128356],"domain_scores_gemma":[0.9983125,0.0006732204,0.0002640602,0.0003264465,0.0003813969,0.00004238487],"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":[0.0004260359,0.000322137,0.2787723,0.0001379041,0.0002430653,0.0001449492,0.0002419624,0.6331221,0.01468438,0.0003520899,0.0002932896,0.07125968],"study_design_scores_gemma":[0.00006106745,0.00007823491,0.1030836,0.00003480929,0.00007986078,0.00003644936,0.0001293734,0.8882867,0.007272687,0.0003007222,0.0006040184,0.00003256807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752215,0.0004974601,0.02300064,0.00007465509,0.00002194012,0.00003069246,0.0003207055,0.0001873762,0.0006451271],"genre_scores_gemma":[0.9896351,0.0001315494,0.009820851,0.00001223302,0.000004955933,0.00001787177,0.0002755329,0.00002761286,0.00007434975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02851013,"threshold_uncertainty_score":0.05668837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02136527021997769,"score_gpt":0.2522125052067567,"score_spread":0.230847234986779,"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."}}