{"id":"W3112874811","doi":"10.1088/1748-9326/abd34f","title":"The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019","year":2020,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Scope (computer science); Software deployment; Computer science; Agency (philosophy); Conceptual framework; Stakeholder; Resource (disambiguation); Earth system science; Environmental resource management; Environmental economics; Environmental planning; Geography; Environmental science","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":[0.0004107971,0.0001787487,0.0002162833,0.00003686666,0.0005132488,0.0000722767,0.0002527998,0.00005015917,0.00006892828],"category_scores_gemma":[0.00003703412,0.000112023,0.00003106147,0.0001030954,0.001245994,0.0002721563,0.001441265,0.0002058928,0.00004438492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829013,"about_ca_system_score_gemma":0.000001641032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003841629,"about_ca_topic_score_gemma":0.00009336737,"domain_scores_codex":[0.9977032,0.0002233873,0.0001928468,0.0004741905,0.0007509437,0.0006554189],"domain_scores_gemma":[0.999257,0.0002759997,0.00003973743,0.0002205376,0.000002010541,0.0002047601],"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":[0.0007839981,0.0001429831,0.2786905,0.00008957835,0.0001770052,0.00006294622,0.007706076,0.0007685722,0.6824371,0.003237988,0.01218577,0.01371752],"study_design_scores_gemma":[0.002315284,0.00401769,0.3960569,0.0002960037,0.00007471453,0.00005683525,0.02125125,0.001315477,0.2985644,0.01031894,0.2637087,0.002023709],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847044,0.00222627,0.0001287694,0.01052523,0.00007742939,0.0003018106,0.0001089851,0.0000203925,0.001906715],"genre_scores_gemma":[0.9972468,0.001617015,0.0001003646,0.0006716636,0.00006711095,0.00007290365,0.000004098076,0.0000236905,0.0001963751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3838726,"threshold_uncertainty_score":0.4590916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01498163151406726,"score_gpt":0.252311004556888,"score_spread":0.2373293730428208,"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."}}