{"id":"W3170196945","doi":"10.3390/su13116400","title":"Maximizing Benefits to Nature and Society in Techno-Ecological Innovation for Water","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Victoria","funders":"Canada Research Chairs","keywords":"Ecosystem services; Naturalness; Environmental resource management; Business; Work (physics); Ecosystem; Environmental economics; Ecology; Environmental science; Engineering; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000286435,0.00009856333,0.0001157884,0.00002635763,0.00008911705,0.00002519437,0.00006573058,0.0001484929,0.0001089927],"category_scores_gemma":[0.0002326034,0.00007222686,0.00003364485,0.000381381,0.00007714274,0.00008656491,0.0003296731,0.0001361952,0.000002752834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009731583,"about_ca_system_score_gemma":0.00002819693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005514833,"about_ca_topic_score_gemma":0.0001484121,"domain_scores_codex":[0.9990252,0.00001988333,0.0001683362,0.0003934028,0.00009955402,0.0002936622],"domain_scores_gemma":[0.9996459,0.00003396298,0.0000156072,0.0001890381,0.00007707036,0.00003835581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006056505,0.0002813789,0.9197779,0.0001046129,0.00001344235,0.00001967558,0.001657238,0.001116005,0.01154165,0.003587821,0.0001845431,0.06165516],"study_design_scores_gemma":[0.0007240754,0.0001034,0.8933392,0.000006545058,0.00000850549,0.00001238842,0.00148406,0.0002410901,0.05892953,0.0423219,0.002642027,0.0001873407],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954236,0.00001090579,0.0001133489,0.003660514,0.0000333448,0.0005858027,0.000008055066,0.00002882247,0.0001356512],"genre_scores_gemma":[0.9941906,0.000001276326,0.005296574,0.000224639,0.000007783021,0.0001200601,0.00002497831,0.000005514813,0.000128584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06146782,"threshold_uncertainty_score":0.2945326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006147317145697515,"score_gpt":0.2387321184576056,"score_spread":0.2325848013119081,"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."}}