{"id":"W4361281639","doi":"10.3390/rs15071825","title":"An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Data assimilation; Environmental science; Leaf area index; Ensemble Kalman filter; Crop yield; Univariate; Yield (engineering); Water content; Remote sensing; Kalman filter; Mathematics; Meteorology; Statistics; Agronomy; Multivariate statistics; Extended Kalman filter; Geography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000510185,0.0003375022,0.000334224,0.00006625679,0.00039151,0.000133175,0.0001895847,0.0002801332,0.000004737341],"category_scores_gemma":[0.0002092233,0.0002356707,0.00008927217,0.0005706299,0.0003105364,0.0002941074,0.0002237542,0.0005020155,0.00001068004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691823,"about_ca_system_score_gemma":0.00001793874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001828885,"about_ca_topic_score_gemma":0.0006113002,"domain_scores_codex":[0.997824,0.0001381749,0.0004422599,0.0006476091,0.0005061729,0.0004417532],"domain_scores_gemma":[0.998809,0.0001502445,0.0002629589,0.0005985242,0.00004350872,0.0001357403],"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":[0.00002592396,0.0000134787,0.0001088387,0.00003133849,0.0000158166,0.000004213543,0.00442109,0.2432097,0.6591001,6.045898e-7,0.002765954,0.09030293],"study_design_scores_gemma":[0.0002195309,0.00004091153,0.002533192,0.0001198056,0.00003077291,0.0000611753,0.001083813,0.9805241,0.01459026,0.0004814718,0.00003052706,0.0002844273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8960498,0.00002026977,0.09806381,0.0009762043,0.00009295317,0.0003673265,0.000003925642,0.0001879106,0.004237779],"genre_scores_gemma":[0.9317233,0.00001645429,0.06742458,0.0003011892,0.00005583976,2.06792e-8,0.00004075101,0.00004633681,0.0003915108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7373144,"threshold_uncertainty_score":0.9610373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01610151944508931,"score_gpt":0.2276882496318478,"score_spread":0.2115867301867585,"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."}}