{"id":"W3004406808","doi":"10.1002/essoar.10502105.1","title":"Shifts in Irrigation Water Demand and Supply Patterns during Critical Crop Growth Stages under Changing Impacts of Climate and Socio-Economic Dynamics in South Asia","year":2020,"lang":"en","type":"article","venue":"","topic":"Water resources management and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Kharif crop; Irrigation; Agriculture; Water resources; Cropping; Groundwater; Environmental science; Food security; Crop; Climate change; Water resource management; Surface water; Geography; Agronomy; Biology; Ecology; Environmental engineering","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":[0.0002420828,0.0001921762,0.0001813545,0.0003661147,0.0001543074,0.0004746439,0.0002908588,0.0002745222,0.001220341],"category_scores_gemma":[0.0004761971,0.0001505955,0.0003918396,0.0006352778,0.0002791763,0.0005451337,0.0005458713,0.0002722366,0.0001281428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005838308,"about_ca_system_score_gemma":0.0004947975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03496448,"about_ca_topic_score_gemma":0.03444011,"domain_scores_codex":[0.99993,0.00001307254,0.000006491033,0.00002059569,0.000009320152,0.00002057557],"domain_scores_gemma":[0.9998088,0.00004625924,0.00004491457,0.00001628337,0.00005136017,0.00003234001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002339397,0.00008865489,0.9230238,0.0001729805,0.0001474909,0.0006907944,0.002587853,0.04413787,0.01003491,0.001782771,0.001392467,0.01570645],"study_design_scores_gemma":[0.00001121202,0.00004342599,0.9443701,0.00003276129,0.0000433292,0.0001114808,0.002891915,0.0492639,0.001018789,0.0005744778,0.00161231,0.00002638495],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988023,0.00002997873,0.0002052435,0.00007536694,0.000001646132,0.000003231514,0.0004518167,0.000006274241,0.0004241593],"genre_scores_gemma":[0.9990777,0.00005309457,0.0001701733,0.00001242646,0.000001546883,0.000007072959,0.000535049,0.000002825839,0.000140141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03496448,"threshold_uncertainty_score":0.0695219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006338567006000402,"score_gpt":0.1960831470545502,"score_spread":0.1897445800485498,"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."}}