{"id":"W3185042836","doi":"10.23919/acc50511.2021.9482905","title":"Soil moisture map construction by sequential data assimilation using an extended Kalman filter","year":2021,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Water content; Sensor fusion; Data assimilation; Fusion center; Computer science; Environmental science; Usability; Remote sensing; Irrigation; Soil science; Agricultural engineering; Engineering; Computer vision; Meteorology; Geotechnical engineering; Artificial intelligence; Geography; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003158606,0.0004086778,0.0004385432,0.0004452405,0.0002796817,0.0003770914,0.0004293824,0.0003278924,0.0008127654],"category_scores_gemma":[0.0008767212,0.0003406134,0.0004058242,0.0005236624,0.0001961312,0.0007473711,0.0006213209,0.0004134156,0.0002531573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003039907,"about_ca_system_score_gemma":0.0008207788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01349079,"about_ca_topic_score_gemma":0.0156782,"domain_scores_codex":[0.9998436,0.00002312635,0.00001049137,0.00005732956,0.0000485435,0.00001695771],"domain_scores_gemma":[0.9997872,0.00005551878,0.00003610774,0.00003108642,0.00008034924,0.000009805778],"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":[0.0001691586,0.00007918631,0.003975199,0.00007180058,0.00008291526,0.00007686537,0.0001317121,0.693583,0.04395738,0.002736767,0.001174822,0.2539612],"study_design_scores_gemma":[0.000009120842,0.00002248402,0.001141461,0.000002174634,0.000008117345,0.00001041862,0.00001044405,0.9934306,0.004151151,0.0006148266,0.0005898054,0.00000957565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04092596,0.00003712235,0.9576061,0.00003758879,0.00001808744,0.00002078964,0.00008586462,0.0007485018,0.0005200857],"genre_scores_gemma":[0.607967,0.00009206458,0.39036,0.00002512975,0.00001792812,0.00007274884,0.0002957352,0.00005957962,0.001109909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01349079,"threshold_uncertainty_score":0.02682453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03703477906679663,"score_gpt":0.2732725401412194,"score_spread":0.2362377610744228,"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."}}