{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003179053,0.0002631119,0.0002530017,0.0005812403,0.0001890626,0.0003756589,0.0002320185,0.0002991482,0.0004295881],"category_scores_gemma":[0.0009709806,0.0001912955,0.0001897968,0.0006264097,0.0001847904,0.0004713325,0.0002082763,0.0002180711,0.0001453476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001111143,"about_ca_system_score_gemma":0.0001798257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002595661,"about_ca_topic_score_gemma":0.004141899,"domain_scores_codex":[0.9998834,0.00002429003,0.000005863697,0.00005469015,0.00001832029,0.00001338272],"domain_scores_gemma":[0.9993862,0.0003453415,0.0000907497,0.00007684161,0.00006469342,0.00003612222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001103255,0.0001825158,0.4621187,0.0001115231,0.0002207784,0.0001612039,0.0002959764,0.02012156,0.4401149,0.0002504585,0.0003073761,0.07501161],"study_design_scores_gemma":[0.00002905765,0.0002542249,0.7152161,0.00001078066,0.0001991203,0.0003506417,0.000195865,0.2329463,0.04958643,0.0005007645,0.0006364688,0.00007435438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883521,0.0001158472,0.01037951,0.00001722324,0.000005615346,0.000005922481,0.0003490369,0.00009987404,0.0006748516],"genre_scores_gemma":[0.9975675,0.00003868654,0.00214028,0.00000515464,0.000005094023,0.000005199376,0.0001494144,0.000008970797,0.00007965081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002595661,"threshold_uncertainty_score":0.005161107,"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."}}