{"id":"W2971086847","doi":"10.3390/w11091789","title":"Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Arid; Precipitation; Lag; Environmental science; Vegetation (pathology); Climatology; Physical geography; Climate change; Geography; Meteorology; Ecology; Geology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000583767,0.0005729169,0.0004250365,0.000430333,0.0002118744,0.0003032046,0.0006385475,0.0004369991,0.000811119],"category_scores_gemma":[0.000813662,0.0002851759,0.0005064013,0.0004755766,0.0001531728,0.0004396493,0.0002829204,0.0004684821,0.0001778092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003828435,"about_ca_system_score_gemma":0.0006193819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01412672,"about_ca_topic_score_gemma":0.01246144,"domain_scores_codex":[0.9998372,0.00003508413,0.00001326309,0.00005637965,0.000034829,0.0000233264],"domain_scores_gemma":[0.9998079,0.00007395378,0.00003106376,0.00001497457,0.00006169478,0.00001030716],"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":[0.0001178396,0.00008985928,0.006309377,0.00008917342,0.0001373233,0.000192177,0.00008899721,0.7825344,0.01565086,0.00200833,0.0006165616,0.1921651],"study_design_scores_gemma":[0.000002177727,0.000006825328,0.0007000597,0.000001318666,0.000005358197,0.000009313801,0.000002705775,0.998316,0.0006380669,0.0002032839,0.0001115265,0.000003374358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08314369,0.0003480387,0.9147657,0.00004853128,0.00004020091,0.00004002344,0.0001266709,0.0006712305,0.000815861],"genre_scores_gemma":[0.7558563,0.0002619892,0.2402407,0.00004868989,0.00003931822,0.000135098,0.0004017625,0.00007795142,0.002938225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01412672,"threshold_uncertainty_score":0.02808899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003658006931103181,"score_gpt":0.2220722984143288,"score_spread":0.2184142914832256,"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."}}