{"id":"W2804638826","doi":"10.1088/1748-9326/aaf2a3","title":"Balancing clean water-climate change mitigation trade-offs","year":2018,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Global Environment Facility","keywords":"Environmental economics; Greenhouse gas; Baseline (sea); Environmental science; Natural resource economics; Efficient energy use; Business; Climate change; Sanitation; Water conservation; Climate change mitigation; Water use; Environmental resource management; Water scarcity; Marginal abatement cost; Water resources; Environmental engineering; Economics; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008866002,0.0002718664,0.0001931974,0.0001141966,0.0009643426,0.00006208969,0.0004711033,0.00008236916,0.002249513],"category_scores_gemma":[0.00001764395,0.0002219352,0.0000825005,0.0001841447,0.001784961,0.0005785237,0.001125916,0.0003210153,0.005609041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000827887,"about_ca_system_score_gemma":0.000001858391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003120438,"about_ca_topic_score_gemma":0.0001881105,"domain_scores_codex":[0.9958602,0.000307514,0.0002771961,0.0007261396,0.001334143,0.001494837],"domain_scores_gemma":[0.9991603,0.00007300785,0.00004337453,0.0004637026,0.00000189479,0.0002577285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006410327,0.0001654513,0.02569724,0.00001032023,0.00002906241,0.00007477383,0.003334098,0.00001063767,0.9417858,0.0001866013,0.01223445,0.01640747],"study_design_scores_gemma":[0.0008969744,0.000679556,0.3303771,0.0000490305,0.00001890192,0.00003088049,0.001578717,0.0005081996,0.6304048,0.002433371,0.03226992,0.0007525727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740597,0.00004931173,0.0000515326,0.01001775,0.0002177647,0.0003991263,0.00004436492,0.00007998194,0.01508049],"genre_scores_gemma":[0.9961744,0.00004865963,0.0002435554,0.002514452,0.0005351625,0.0001424092,0.00004700906,0.00005777122,0.0002365997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.311381,"threshold_uncertainty_score":0.9986626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03416421515397638,"score_gpt":0.271640499657759,"score_spread":0.2374762845037826,"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."}}