{"id":"W2830500633","doi":"10.1038/s41598-018-28612-6","title":"Enhancing the systems productivity and water use efficiency through coordinated soil water sharing and compensation in strip-intercropping","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Agronomic Practices and Intercropping Systems","field":"Agricultural and Biological Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Tarim University; Gansu Agricultural University; National Natural Science Foundation of China","keywords":"Intercropping; Agronomy; Sativum; Soil water; Environmental science; Water use; Yield (engineering); Productivity; Water-use efficiency; Crop; Biology; Irrigation; Soil science; Materials 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00267565,0.0001363064,0.0001851389,0.00002313821,0.0007152163,0.001283063,0.0001294748,0.00005700353,0.00002699766],"category_scores_gemma":[0.000103504,0.00003956077,0.0000275675,0.0001524496,0.0003323923,0.0008840653,0.0002569858,0.0001190978,0.00001166998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004523003,"about_ca_system_score_gemma":0.000005936369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004326482,"about_ca_topic_score_gemma":0.001993819,"domain_scores_codex":[0.9981189,0.0001194916,0.0004759219,0.0007517663,0.0001737767,0.0003601834],"domain_scores_gemma":[0.9994572,0.00005973041,0.0001448019,0.0001716936,0.0001226652,0.00004386044],"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":[0.00001088057,0.00002751239,0.01721322,0.0000187836,0.000007443776,0.00001271978,0.002710613,0.00001571027,0.9790311,0.00002495559,0.0001105768,0.000816528],"study_design_scores_gemma":[0.0002912191,0.0003453059,0.06279745,0.000686534,0.00004156983,0.001121053,0.007017781,0.01005534,0.8721192,0.0009644936,0.04373923,0.0008207844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960312,0.00008177525,0.00005628393,0.0005364468,0.002678174,0.0003963395,8.613928e-7,0.000036077,0.0001828268],"genre_scores_gemma":[0.9987381,0.000002471496,0.000006231763,0.00001585857,0.0001901582,0.00001228495,0.000017289,0.000001518516,0.001016092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1069118,"threshold_uncertainty_score":0.9997537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03382815641784202,"score_gpt":0.2378133563798173,"score_spread":0.2039851999619753,"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."}}