{"id":"W2608383430","doi":"10.14288/1.0343993","title":"From streams to citizens : a multi-lens investigation of water quality through carbon cycles and participation within water science and policy","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Water quality; STREAMS; Environmental science; Lens (geology); Quality (philosophy); Environmental resource management; Environmental economics; Business; Political science; Computer science; Economics; Engineering; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008804186,0.0005046737,0.0004689705,0.003340542,0.01951526,0.01713463,0.00132281,0.002921203,0.004649806],"category_scores_gemma":[0.00839832,0.0004909316,0.0004914062,0.003811732,0.02637412,0.01918637,0.01139276,0.006167691,0.0002372846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01830596,"about_ca_system_score_gemma":0.009014225,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04171627,"about_ca_topic_score_gemma":0.07067963,"domain_scores_codex":[0.9898436,0.007996466,0.0001585406,0.0004616676,0.0006976596,0.0008420363],"domain_scores_gemma":[0.9913365,0.006828765,0.0004075179,0.000330756,0.0006073406,0.0004891642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003038456,0.00004898569,0.005590283,0.0001218747,0.000009378166,0.0004931565,0.8838945,0.0001339979,0.0004064824,0.09087473,0.002935277,0.01546094],"study_design_scores_gemma":[0.000003241567,0.00001986462,0.001579386,0.0001356446,0.000005611372,0.00006933479,0.9514717,0.0001971081,0.0001730923,0.01502073,0.03131307,0.00001123927],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7668914,0.002610747,0.01007922,0.07138332,0.0002814371,0.000280712,0.0002965755,0.00005563254,0.148121],"genre_scores_gemma":[0.9886414,0.001523873,0.002248275,0.002472735,0.00004783164,0.0001265989,0.00005657674,0.00004303081,0.004839596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9582837,"threshold_uncertainty_score":0.1328197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02997817887659057,"score_gpt":0.2368612351899463,"score_spread":0.2068830563133557,"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."}}