{"id":"W2133486523","doi":"10.1073/pnas.1307712111","title":"Quantifying causal mechanisms to determine how protected areas affect poverty through changes in ecosystem services and infrastructure","year":2014,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":319,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Ecosystem services; Poverty; Context (archaeology); Tourism; Recreation; Ecotourism; Deforestation (computer science); Business; Affect (linguistics); Environmental resource management; Natural resource economics; Provisioning; Public economics; Environmental planning; Ecosystem; Geography; Economics; Economic growth; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01032675,0.001070165,0.0006472867,0.003405655,0.0008477091,0.002320208,0.0009477802,0.0009958986,0.006295272],"category_scores_gemma":[0.02711935,0.0004868699,0.001576929,0.002554386,0.002212936,0.003544057,0.002748456,0.001442391,0.0001862441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003082383,"about_ca_system_score_gemma":0.004223572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01308008,"about_ca_topic_score_gemma":0.01736816,"domain_scores_codex":[0.9955114,0.002862007,0.0002866117,0.0004966062,0.00043984,0.0004036345],"domain_scores_gemma":[0.972514,0.01869558,0.005669611,0.001530244,0.001193016,0.0003975971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001524668,0.0006277241,0.7129193,0.001136202,0.002238278,0.0002065177,0.0007939362,0.07683019,0.001374804,0.1410572,0.001171224,0.06149217],"study_design_scores_gemma":[0.00008731247,0.0005908922,0.5792712,0.0005853553,0.001951759,0.0001519576,0.003608826,0.1169389,0.002697048,0.2828269,0.01118255,0.0001072041],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8334193,0.004596329,0.1176817,0.005155665,0.000107046,0.001236358,0.004778164,0.0001954304,0.03283016],"genre_scores_gemma":[0.9824342,0.001313072,0.01445768,0.0002485398,0.00002399535,0.0003806449,0.0004811159,0.00001347774,0.0006472879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01308008,"threshold_uncertainty_score":0.05461371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03217298004181836,"score_gpt":0.2468001781175421,"score_spread":0.2146271980757237,"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."}}