{"id":"W2018605676","doi":"10.3390/rs70302238","title":"Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"National Key Research and Development Program of China; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; Priority Academic Program Development of Jiangsu Higher Education Institutions; Chinese Academy of Sciences; National Natural Science Foundation of China; Canadian Foundation for Climate and Atmospheric Sciences; Natural Resources Canada; Université Laval; U.S. Department of Energy; National Science Foundation","keywords":"Nonlinear system; Environmental science; Statistical physics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001882336,0.000135887,0.0001963772,0.00001089597,0.000154144,0.00003000314,0.00007300227,0.00005351608,0.000003001344],"category_scores_gemma":[0.00003150144,0.00004762379,0.00004842856,0.000103477,0.00006530365,0.0001259223,0.0001176328,0.00007402032,0.000004847302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004255661,"about_ca_system_score_gemma":0.0000053352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003854623,"about_ca_topic_score_gemma":0.0002197067,"domain_scores_codex":[0.9989867,0.00005222487,0.0002194871,0.000225433,0.0002923912,0.000223732],"domain_scores_gemma":[0.9995559,0.00002997407,0.00007976816,0.00007183359,0.000129886,0.0001326274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002840519,0.00009228344,0.01288025,0.00005611764,0.00002031673,0.000008079274,0.0008807013,0.001256092,0.7153189,0.000007072559,0.00009886841,0.2690972],"study_design_scores_gemma":[0.0003500868,0.0004218124,0.004379099,0.0001199849,0.00001526818,0.00002357032,0.0001721316,0.9302608,0.06269436,0.00003488147,0.001301632,0.0002263519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987954,0.0002316063,0.0000314993,0.0004016438,0.00008522424,0.0001188925,0.00000367117,0.00003020575,0.0003018911],"genre_scores_gemma":[0.9978896,0.00005485883,0.001652027,0.00006456837,0.000153285,1.303355e-8,0.00000812094,0.000001614351,0.0001759247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9290047,"threshold_uncertainty_score":0.1942042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0701447723093576,"score_gpt":0.2286734624218519,"score_spread":0.1585286901124943,"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."}}