{"id":"W2077467872","doi":"10.1080/19390450903350812","title":"Can Payments for Watershed Services Help Finance Biodiversity Conservation? A Spatial Analysis of Highland Guatemala","year":2010,"lang":"en","type":"article","venue":"Journal of Natural Resources Policy Research","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nature Conservancy","keywords":"Biodiversity; Payment; Ecosystem services; Biodiversity conservation; Quarter (Canadian coin); Measurement of biodiversity; Watershed; Natural resource economics; Business; Environmental resource management; Work (physics); Geography; Environmental planning; Ecology; Environmental science; Economics; Ecosystem; Finance; Biology","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":[0.0003285241,0.0001676977,0.0002555411,0.00159327,0.0007731959,0.001514329,0.000603305,0.0003553068,0.004756028],"category_scores_gemma":[0.003645767,0.0001472942,0.0003403568,0.004196573,0.0009707027,0.0006843865,0.001231175,0.0003701047,0.0002194657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003825976,"about_ca_system_score_gemma":0.001377016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4229603,"about_ca_topic_score_gemma":0.4064254,"domain_scores_codex":[0.999626,0.0001442826,0.00001263888,0.00003931828,0.00003867088,0.0001389838],"domain_scores_gemma":[0.9983277,0.0006318132,0.0005310779,0.00007025196,0.000230054,0.0002091715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001188358,0.00008292287,0.9747837,0.00008535977,0.0001368268,0.001319583,0.002615512,0.002722331,0.0003543281,0.004745542,0.002400397,0.01063474],"study_design_scores_gemma":[0.00001037158,0.00002558128,0.9875184,0.00004190105,0.00005256943,0.000149138,0.005185152,0.004638758,0.00006236919,0.00058321,0.001723246,0.000009404645],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964898,0.0001891958,0.00006310239,0.0009757141,0.000002311547,0.000008338334,0.0003842614,0.00001113129,0.001876154],"genre_scores_gemma":[0.9995546,0.00009148699,0.00004956796,0.00001752574,0.000002739099,0.000004923466,0.0001127081,0.000002179643,0.0001643247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4229603,"threshold_uncertainty_score":0.8409967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567763111763881,"score_gpt":0.3076080239563633,"score_spread":0.2819303928387245,"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."}}