{"id":"W2587119238","doi":"10.1007/s11625-017-0423-7","title":"Sustainability beyond city limits: can “greener” beef lighten a city’s Ecological Footprint?","year":2017,"lang":"en","type":"article","venue":"Sustainability Science","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Solar Energy Technologies Office; Social Sciences and Humanities Research Council of Canada; Canada Research Chairs","keywords":"Ecological footprint; Sustainability; Metric (unit); Footprint; Business; Unintended consequences; Ecosystem services; Environmental resource management; Sustainable development; Environmental economics; Environmental planning; Economics; Ecology; Geography; Ecosystem; Marketing; Political science","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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.005456201,0.0003142357,0.0005709044,0.0002340677,0.002295029,0.0006186704,0.001963015,0.0002033835,0.0004345536],"category_scores_gemma":[0.007061268,0.0003398058,0.0002120504,0.0003161598,0.003593415,0.001096612,0.0009705422,0.0003457187,0.00007199223],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005259562,"about_ca_system_score_gemma":0.0006043364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002506902,"about_ca_topic_score_gemma":0.0006130863,"domain_scores_codex":[0.9962179,0.00005097918,0.0008701527,0.00160799,0.0001686073,0.001084341],"domain_scores_gemma":[0.9962945,0.0001129374,0.0006887952,0.00220417,0.0003017636,0.0003978105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001557491,0.0002578994,0.8899347,0.00004170888,0.000008896379,0.000005543671,0.0004614042,0.0001197846,0.000009823088,0.1044934,0.00005368393,0.004597567],"study_design_scores_gemma":[0.0003004403,0.0001218487,0.6680549,0.000001919522,0.000004523934,0.000002217726,0.000777138,0.0005899561,0.0001309325,0.3274359,0.002294213,0.0002859896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615291,0.0000744834,0.0005279602,0.01658867,0.0004209318,0.0009854096,0.00005358149,0.00006719863,0.01975264],"genre_scores_gemma":[0.9971391,0.00002412476,0.0004420577,0.0002706454,0.00009983181,0.0001208656,0.000006280015,0.00001593429,0.001881168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2229426,"threshold_uncertainty_score":0.9999054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07239916821623552,"score_gpt":0.2684739228861658,"score_spread":0.1960747546699303,"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."}}