{"id":"W1512577015","doi":"10.1016/s0065-2113(09)01007-4","title":"Chapter 7 Nutrient and Water Management Effects on Crop Production, and Nutrient and Water Use Efficiency in Dryland Areas of China","year":2009,"lang":"en","type":"book-chapter","venue":"Advances in agronomy","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":245,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Environmental science; Nutrient; Arable land; Agronomy; Irrigation; Nutrient management; Water balance; Water use; Agriculture; Water resources; Agroforestry; Soil water; Soil science; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001479418,0.0002700229,0.0002991538,0.00007362593,0.00007486075,0.00004527895,0.00007770647,0.00009153963,0.00005205322],"category_scores_gemma":[0.000006845134,0.00009882299,0.00003198065,0.00003109808,0.0001101042,0.0002268001,0.00009972797,0.0001387199,0.000003000815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003192775,"about_ca_system_score_gemma":0.000001109805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002230975,"about_ca_topic_score_gemma":0.00007880299,"domain_scores_codex":[0.9987082,0.00001794866,0.0003297097,0.0005476321,0.0001564496,0.0002400157],"domain_scores_gemma":[0.9997023,0.00003901927,0.00008800446,0.00008297562,0.00002637158,0.00006134412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004877194,0.0005690125,0.01324168,0.0006685816,0.00006168797,0.00004982262,0.001896343,0.00005442861,0.01290012,0.05381282,0.00008965474,0.9161682],"study_design_scores_gemma":[0.003221956,0.003061104,0.2600228,0.003420993,0.00009519788,0.00002083822,0.0003259872,0.00002124891,0.130643,0.04730299,0.5499396,0.001924208],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809772,0.00162739,0.000004743261,0.000914273,0.0001290589,0.001194814,0.000004890502,0.00001443185,0.0151332],"genre_scores_gemma":[0.9797655,0.00486877,0.00006218051,0.0001559903,0.00007308012,0.00004039874,0.00003845421,0.000002945217,0.01499263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9142439,"threshold_uncertainty_score":0.4029885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00810121845531341,"score_gpt":0.1995492790405697,"score_spread":0.1914480605852563,"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."}}