{"id":"W2803025004","doi":"10.3390/rs10050708","title":"Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Oak Ridge National Laboratory; National Key Research and Development Program of China; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; University of Oklahoma","keywords":"Environmental science; Eddy covariance; Wetland; Primary production; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Vegetation (pathology); Carbon cycle; Satellite; Ecosystem; Remote sensing; Atmospheric sciences; Climatology; Climate change; Normalized Difference Vegetation Index; Oceanography; Geology; Ecology; Vegetation Index","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":[0.0003573048,0.0001823891,0.0002584746,0.0000447364,0.0001509737,0.00004552537,0.0001323843,0.0001156145,0.00001959357],"category_scores_gemma":[0.0001169163,0.0001545924,0.00002311814,0.0002850729,0.0003884308,0.0005050828,0.0005292878,0.0001879367,0.00001500525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128594,"about_ca_system_score_gemma":0.00001911378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001233636,"about_ca_topic_score_gemma":0.0005952901,"domain_scores_codex":[0.9984781,0.00007830688,0.0002954904,0.0005591485,0.0003012508,0.0002877631],"domain_scores_gemma":[0.9993125,0.00001424433,0.00008949319,0.0004917189,0.00002600127,0.00006610146],"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":[0.0002283913,0.00003256798,0.0003912112,0.00003314933,0.00001462011,0.00002208572,0.001747753,0.04806553,0.9199154,6.751122e-7,0.0003500145,0.02919857],"study_design_scores_gemma":[0.0001933498,0.0000460432,0.003820243,0.0001692555,0.00002034746,0.000441,0.00009589856,0.9882509,0.00649712,0.0001521913,0.0001108804,0.0002027712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992415,0.00002369545,0.006125091,0.0001930308,0.0002074824,0.0002008481,0.000006320346,0.00003526975,0.0007933348],"genre_scores_gemma":[0.9110355,0.00001232099,0.08841269,0.00002656153,0.000219402,1.333295e-9,0.00004273683,0.00001999383,0.0002307425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9401854,"threshold_uncertainty_score":0.6304094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648516661412694,"score_gpt":0.2337072839702171,"score_spread":0.2172221173560901,"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."}}