{"id":"W2126573617","doi":"10.5589/m04-038","title":"Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent heat; Estimation; Flux (metallurgy); Remote sensing; Environmental science; Data mining; Geography; Econometrics; Computer science; Data science; Mathematics; Meteorology; Engineering; Systems engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01773977,0.0009305248,0.001056129,0.001299705,0.0004491585,0.002210826,0.001488001,0.001117102,0.0005165329],"category_scores_gemma":[0.06609989,0.0005812875,0.000948277,0.001474588,0.001622534,0.002502904,0.001930717,0.001303858,0.0001894331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228878,"about_ca_system_score_gemma":0.001128128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01531944,"about_ca_topic_score_gemma":0.01018848,"domain_scores_codex":[0.9911658,0.004933625,0.0005754461,0.0008496971,0.002269141,0.0002062951],"domain_scores_gemma":[0.9422927,0.0463336,0.003519534,0.004989564,0.002690355,0.0001743088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003219356,0.0001283465,0.02298862,0.0007298539,0.0004017615,0.0004939786,0.0004522833,0.8385941,0.01132078,0.0320451,0.001259037,0.09126417],"study_design_scores_gemma":[0.00004692344,0.00009738733,0.01748278,0.0002280607,0.00007961997,0.0002446417,0.0001460842,0.9402163,0.005161276,0.033953,0.002235579,0.0001083729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09666256,0.001325189,0.8983203,0.001481806,0.0001102645,0.0001217369,0.0003472636,0.0002265115,0.001404345],"genre_scores_gemma":[0.7882127,0.001189608,0.2088332,0.0004061596,0.0001611431,0.0002267076,0.0003524454,0.0000890712,0.0005290474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01773977,"threshold_uncertainty_score":0.09381801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0574879792941328,"score_gpt":0.241591563093974,"score_spread":0.1841035837998412,"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."}}