{"id":"W3047305903","doi":"10.1051/e3sconf/202018302001","title":"Challenges and Opportunities in the Operationalization of the Water-Environment-Energy-Food (WE<sup>2</sup>F) Nexus: Case Study of the Upper Niger Basin and Inner Niger Delta, West Africa","year":2020,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Nexus (standard); Integrated water resources management; Natural resource; Sustainability; Water resources; Environmental resource management; Water security; Operationalization; Food security; Business; Environmental planning; Water use; Natural resource economics; Environmental economics; Geography; Environmental protection; Political science; Environmental science; Engineering; Agriculture; Economics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002887508,0.0002100642,0.0002960683,0.00003797915,0.0001992787,0.0000279744,0.000388373,0.00006062701,0.0001934811],"category_scores_gemma":[0.00002920835,0.00009147828,0.00004222934,0.0001081311,0.0007427069,0.0002148882,0.0005569987,0.00009837795,9.667223e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001466385,"about_ca_system_score_gemma":0.00004357102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008619556,"about_ca_topic_score_gemma":0.003020743,"domain_scores_codex":[0.9980536,0.0005127648,0.0004283104,0.0003083897,0.0005040098,0.0001929086],"domain_scores_gemma":[0.9993411,0.0001420823,0.0001524796,0.0002951074,0.00002473687,0.00004447444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001637155,0.002017029,0.336242,0.0002235663,0.0005266126,0.00007040508,0.563594,0.008936808,0.004457146,0.06632555,0.001102958,0.01634015],"study_design_scores_gemma":[0.002481902,0.00235896,0.158648,0.0001978085,0.0002646452,0.0001126841,0.7863048,0.006437648,0.01130704,0.01330106,0.01778426,0.0008011295],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790651,0.001009758,0.000009537828,0.01094155,0.00003068544,0.0002912342,0.00003361437,0.000005932284,0.008612548],"genre_scores_gemma":[0.9989356,0.0005799312,0.00001120945,0.0002636078,0.00002012188,0.00004704263,0.000002809785,0.00001057243,0.0001290405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2227108,"threshold_uncertainty_score":0.3730376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07277108178329782,"score_gpt":0.2228115145753738,"score_spread":0.150040432792076,"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."}}