{"id":"W4251728574","doi":"10.1504/ijw.2017.085878","title":"Assessing irrigation network performance based on different climate change and water supply scenarios: a case study in Northern Iran","year":2017,"lang":"en","type":"article","venue":"International Journal of Water","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Irrigation; Climate change; Environmental science; Water resource management; Baseline (sea); Equity (law); Irrigation district; Water resources; Environmental resource management; Ecology","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":[0.001268565,0.0004447514,0.0003230568,0.001022189,0.0006384851,0.0009371347,0.0007617017,0.0008029309,0.0006721914],"category_scores_gemma":[0.002832899,0.0002132815,0.0005472382,0.001894493,0.0006635971,0.001124307,0.0005720352,0.0004484267,0.00006894725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003983304,"about_ca_system_score_gemma":0.001543636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05538612,"about_ca_topic_score_gemma":0.08493141,"domain_scores_codex":[0.9991186,0.0003491539,0.00003571202,0.0001020619,0.0001849281,0.00020949],"domain_scores_gemma":[0.9985173,0.0006549156,0.0002886157,0.00008221023,0.0003194204,0.0001374957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004501933,0.0008999814,0.4584283,0.0002737698,0.000302995,0.007922159,0.002544706,0.4768213,0.006645018,0.003602996,0.002110994,0.03999754],"study_design_scores_gemma":[0.00007374465,0.000761033,0.501516,0.00005997262,0.0001865687,0.001085535,0.02064421,0.463452,0.004981632,0.00255341,0.004552247,0.0001336076],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977031,0.00003965625,0.0008531238,0.00008236684,0.000004605906,0.00003348311,0.0001478245,0.00001595851,0.001119773],"genre_scores_gemma":[0.9986439,0.00006249841,0.0009115394,0.00000621556,0.000002995052,0.00001323743,0.0001327943,0.00000347461,0.000223295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05538612,"threshold_uncertainty_score":0.1101275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06942997886053315,"score_gpt":0.2971327549395637,"score_spread":0.2277027760790306,"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."}}