{"id":"W2586109962","doi":"10.5751/es-08979-220118","title":"Social-ecological enabling conditions for payments for ecosystem services","year":2017,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Ecosystem services; Payment; Ecosystem; Payment for ecosystem services; Ecology; Environmental resource management; Ecological economics; Social ecology; Business; Environmental science; Sustainability; Political science; Biology; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003356015,0.00009177911,0.0001786932,0.000003999614,0.002753735,0.00007851154,0.000209474,0.0001921452,0.0002208754],"category_scores_gemma":[0.000009370523,0.00007228392,0.0001205952,0.00001222614,0.00004313478,0.0002083863,0.0001378673,0.0000462306,0.00003814868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004280616,"about_ca_system_score_gemma":0.000005774043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002937678,"about_ca_topic_score_gemma":0.001752102,"domain_scores_codex":[0.9992844,0.0000188348,0.0001396626,0.0002445494,0.00004536051,0.0002672299],"domain_scores_gemma":[0.9995427,0.0001307153,0.0001434121,0.0001211889,0.00001056139,0.00005147903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004995664,0.0002830682,0.9713795,0.0007224836,0.0002808886,0.000002308984,0.004119234,0.0000637414,0.001255008,0.002132567,0.01911668,0.000594629],"study_design_scores_gemma":[0.001744996,0.0001782483,0.9365413,0.00001305776,0.00008361171,0.000003969613,0.001233714,0.01064368,0.0001402671,0.007013581,0.04216236,0.0002412117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971662,0.00001338102,0.00003503438,0.001077982,0.0002210343,0.0004821969,0.0001065609,0.00002548685,0.0008721188],"genre_scores_gemma":[0.998314,0.00003153081,0.0002823256,0.0006928099,0.0001474897,0.0002974737,0.00004030057,0.000006749002,0.0001873759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03483813,"threshold_uncertainty_score":0.9985446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964503811122923,"score_gpt":0.2788815676129178,"score_spread":0.2592365295016886,"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."}}