{"id":"W2905872407","doi":"10.1504/ijgei.2018.10018218","title":"A bibliometric analysis of research on the energy-water nexus from 1963 to 2016 based on SCI-E/SSCI databases","year":2018,"lang":"en","type":"article","venue":"International Journal of Global Energy Issues","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nexus (standard); Renewable energy; Web of science; China; Bibliometrics; Perspective (graphical); Field (mathematics); Water-energy nexus; Database; Political science; Library science; Engineering; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007221145,0.0002262716,0.0003691845,0.01013979,0.0001653961,0.0001078771,0.001538058,0.00005679329,0.001998253],"category_scores_gemma":[0.0002804303,0.0001299275,0.000224777,0.02361413,0.0005136215,0.000267252,0.0005876728,0.0001218079,0.0001090569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005425936,"about_ca_system_score_gemma":0.00004045729,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01216263,"about_ca_topic_score_gemma":0.003135565,"domain_scores_codex":[0.9954554,0.0002843346,0.000583362,0.0003582425,0.002915175,0.0004035189],"domain_scores_gemma":[0.9981515,0.000492592,0.0002076414,0.00044171,0.0005198048,0.0001867485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005515496,0.003462055,0.03326183,0.000007635093,0.01223839,0.0007442564,0.0008704902,0.2095453,0.04242994,0.1386764,0.4903804,0.06286781],"study_design_scores_gemma":[0.001765378,0.003754225,0.1645715,0.0005481478,0.0005962014,0.00002494451,0.0005846142,0.01966972,0.3363698,0.03826338,0.4329419,0.0009102758],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8392251,0.0009894068,0.00602984,0.0184554,0.002794449,0.0001081829,0.001623488,0.00005634805,0.1307178],"genre_scores_gemma":[0.9969053,0.00008939953,0.000381361,0.001514356,0.0005934451,0.000006588928,0.00002512701,0.00001380983,0.0004706177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2939398,"threshold_uncertainty_score":0.9989141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06482332153456268,"score_gpt":0.3773011012925718,"score_spread":0.3124777797580092,"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."}}