{"id":"W2163476075","doi":"","title":"Environmental and socio-economic consequences of forest carbon payments in Bolivia: Results of the OSIRIS-Bolivia model","year":2012,"lang":"en","type":"preprint","venue":"Econstor (Econstor)","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Environment Research Council; International Development Research Centre; Gordon and Betty Moore Foundation","keywords":"Deforestation (computer science); Incentive; Natural resource economics; Agriculture; Payment; Agricultural land; Economics; Business; Geography; Agricultural economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006947222,0.0004923987,0.000723189,0.0001601794,0.00009803117,0.00002646034,0.0008149479,0.0003775923,0.0008059457],"category_scores_gemma":[0.00003083192,0.0004345834,0.0002330201,0.00006105451,0.003024597,0.0002173026,0.00192039,0.0005172517,0.0001373434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007079628,"about_ca_system_score_gemma":0.0001316315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004522312,"about_ca_topic_score_gemma":0.002109284,"domain_scores_codex":[0.9971756,0.0001686274,0.001073305,0.0007047466,0.000308696,0.0005690298],"domain_scores_gemma":[0.9978004,0.0001325563,0.001041757,0.0008630347,0.000003458347,0.0001587604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004605457,0.0001224577,0.9898099,0.00007142474,0.00006745433,0.000001570204,0.00118147,0.006200432,0.00052486,0.0002699027,0.001498641,0.0002058364],"study_design_scores_gemma":[0.001282589,0.00004977932,0.9862496,0.000159943,0.0001307858,0.00001006747,0.0001352581,0.007702829,0.0007771476,0.00237608,0.0005075754,0.0006184007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700831,0.0005421225,0.00000128993,0.0002220806,0.0007201634,0.0008052014,0.0005462574,0.00001794706,0.02706182],"genre_scores_gemma":[0.9977204,0.0004300355,0.000167308,0.00007525705,0.000086489,0.00006181193,0.00002680096,0.00003677548,0.001395126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02763728,"threshold_uncertainty_score":0.9998106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292501638853926,"score_gpt":0.223515144317791,"score_spread":0.2105901279292517,"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."}}