{"id":"W4246685681","doi":"10.5716/wp18033.pdf","title":"Assessing the Downstream Socioeconomic Impacts of Agroforestry in Kenya","year":2018,"lang":"en","type":"report","venue":"","topic":"Energy and Environment Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Impact; University of British Columbia","funders":"World Agroforestry Centre; Consortium of International Agricultural Research Centers; Universiteit Antwerpen; Friedreich's Ataxia Research Alliance","keywords":"Livelihood; Socioeconomic status; Poverty; Agroforestry; Impact evaluation; Ecosystem services; Welfare; Asset (computer security); Business; Geography; Socioeconomics; Agricultural science; Agricultural economics; Agriculture; Ecosystem; Economics; Economic growth; Mathematics; Environmental science; Ecology; Environmental health; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001926712,0.0003288228,0.0002067232,0.0005224247,0.0013762,0.0006922059,0.0003842351,0.0003012174,0.001720438],"category_scores_gemma":[0.002317872,0.0001977229,0.0001541354,0.0007435034,0.0008464838,0.0006525276,0.001099145,0.0003604467,0.0001175406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001996708,"about_ca_system_score_gemma":0.002084748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01985488,"about_ca_topic_score_gemma":0.08655865,"domain_scores_codex":[0.9989875,0.0005697199,0.00004103841,0.00008990058,0.0001328052,0.0001789633],"domain_scores_gemma":[0.9992944,0.0002422407,0.0002590467,0.00002273669,0.00007995382,0.0001015851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008203766,0.001684636,0.9150136,0.0006091929,0.0001294957,0.001247072,0.008029248,0.001473003,0.005952093,0.002027533,0.0004753852,0.06253853],"study_design_scores_gemma":[0.00004143019,0.001084745,0.988421,0.0002207379,0.00006335679,0.0001454705,0.006529792,0.0005635212,0.001296619,0.0005114214,0.001109718,0.00001208297],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977651,0.0001906994,0.0000823857,0.00006660017,0.000001138818,0.00009690249,0.00008793311,8.845018e-7,0.001708336],"genre_scores_gemma":[0.9987943,0.0003619672,0.0004518335,0.00002023581,0.000001312115,0.00008372165,0.00006088342,4.733323e-7,0.0002252645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01985488,"threshold_uncertainty_score":0.0394786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297022308627405,"score_gpt":0.2999895365609612,"score_spread":0.2770193134746872,"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."}}