{"id":"W4353100669","doi":"10.1080/02508060.2023.2167037","title":"Science mapping of water governance research in retrospect","year":2023,"lang":"en","type":"article","venue":"Water International","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Corporate governance; Normalization (sociology); Preprocessor; Network governance; Political science; Poverty; Citation; Regional science; Environmental resource management; Environmental planning; Public administration; Business; Sociology; Geography; Environmental science; Computer science; Social 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001959688,0.00006522582,0.00008075019,0.000170203,0.00009144868,0.00003829,0.0006662599,0.00003087155,0.002699654],"category_scores_gemma":[0.00008098309,0.0000445099,0.00002804206,0.0007880266,0.0006523409,0.0004263943,0.0008414593,0.0001618165,0.001263435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007249854,"about_ca_system_score_gemma":0.00001620281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005825632,"about_ca_topic_score_gemma":0.0001356195,"domain_scores_codex":[0.9978616,0.00003141724,0.0002010321,0.0003286783,0.001083919,0.0004933713],"domain_scores_gemma":[0.9996549,0.00002667054,0.00002261,0.0002041688,0.0000550433,0.0000366446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007872024,0.0001757022,0.3850917,0.00005060176,0.000008135189,0.000100854,0.02513926,0.001936779,0.5764375,0.004449308,0.004130004,0.002401389],"study_design_scores_gemma":[0.0003972006,0.00005107844,0.6020289,0.00005685511,6.763672e-7,0.00000563192,0.001815289,0.002627921,0.3290674,0.01647504,0.04730611,0.0001678979],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732617,0.000002567429,0.000004877225,0.005908122,0.0002852261,0.00009746663,0.000009255854,0.00002055499,0.02041022],"genre_scores_gemma":[0.9961614,0.00002254792,0.00003990763,0.00009481128,0.00004244409,0.00001764623,0.000008479991,0.000006321983,0.003606443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2473702,"threshold_uncertainty_score":0.9995142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07252955179619743,"score_gpt":0.3353723764627007,"score_spread":0.2628428246665033,"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."}}