{"id":"W2792889281","doi":"10.1016/j.scitotenv.2018.03.004","title":"Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Higher Education Discipline Innovation Project","keywords":"Water content; Irrigation; Environmental science; Canopy; Agronomy; Growing season; Soil water; Evapotranspiration; Stage (stratigraphy); Crop; Water-use efficiency; Soil science; Geography; Ecology; Biology; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001894611,0.0001281712,0.0001810136,0.00001328285,0.0002193948,0.00001179459,0.0004291413,0.00005784618,0.0001151819],"category_scores_gemma":[0.0001776726,0.00005646597,0.00009124826,0.0001237833,0.005269718,0.0001220221,0.0004963746,0.00009480074,0.000004295235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001747289,"about_ca_system_score_gemma":0.000008768174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005451557,"about_ca_topic_score_gemma":0.0000206418,"domain_scores_codex":[0.9984705,0.00009522861,0.0003408635,0.0003373555,0.0005235026,0.0002325468],"domain_scores_gemma":[0.999011,0.00009677399,0.0001881921,0.0006292668,0.00002485268,0.00004994891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003523969,0.00006651409,0.000666872,0.00001584015,0.000007928011,1.881232e-8,0.001357192,0.01172307,0.9857691,0.0000524953,0.00006331683,0.0002424149],"study_design_scores_gemma":[0.0001490473,0.0001443812,0.2279761,0.00003689785,0.00003224472,0.000004534184,0.00024566,0.002618997,0.7653487,0.003346845,0.00001329096,0.00008333761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964768,0.000006243394,0.0004597712,0.001667346,0.0001528152,0.0005488635,0.00001009454,0.000004525511,0.0006735255],"genre_scores_gemma":[0.9985237,0.000002339105,0.0009469434,0.00003957713,0.00002225971,0.000002555523,8.143977e-7,0.000005224505,0.0004566014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2273092,"threshold_uncertainty_score":0.9974374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799569390176068,"score_gpt":0.2094290707946775,"score_spread":0.1914333768929169,"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."}}