{"id":"W4393900103","doi":"","title":"Quantifying and modeling crop yields, water and nitrogen fluxes of in situ organic cropping systems","year":2018,"lang":"fr","type":"preprint","venue":"theses.fr (ABES)","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Cropping; In situ; Environmental science; Nitrogen; Agronomy; Crop; Cropping system; Soil science; Agroforestry; Geography; Ecology; Chemistry; Biology; Agriculture; Meteorology","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":[0.0006770136,0.0004850604,0.0008843433,0.0000522814,0.0002460121,0.0003135532,0.0004204571,0.000548662,0.0002402634],"category_scores_gemma":[0.00005544527,0.0001959613,0.000132578,0.0001383996,0.0002776089,0.0002129521,0.0009677193,0.0004161087,0.00006766237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007651874,"about_ca_system_score_gemma":0.00001955971,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04367122,"about_ca_topic_score_gemma":0.005013647,"domain_scores_codex":[0.9974136,0.0001919211,0.0008371337,0.0007389159,0.0001494589,0.0006689787],"domain_scores_gemma":[0.9990516,0.000190171,0.00027153,0.0001575062,0.0001322132,0.0001969845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006936551,0.0001379034,0.03062441,0.0008558423,0.0002262214,0.00001317063,0.008005603,0.002333822,0.9324427,0.02140981,0.00004257712,0.003838602],"study_design_scores_gemma":[0.00507265,0.003660072,0.1536773,0.02235963,0.001719191,0.00124038,0.1110144,0.3068424,0.2616275,0.07506286,0.04276298,0.01496062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991125,0.004472513,0.0000113363,0.001089789,0.0004064517,0.0004835854,0.00005937353,0.00002941399,0.002322492],"genre_scores_gemma":[0.9964795,0.002305869,0.0000737081,0.00007493498,0.0008196894,0.00001401902,0.00006541426,0.000007650118,0.0001591964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6708152,"threshold_uncertainty_score":0.962697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07466378596083141,"score_gpt":0.2570506416549125,"score_spread":0.1823868556940811,"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."}}