{"id":"W2055856386","doi":"10.1007/s10584-012-0456-y","title":"Seeing the trees for the carbon: agroforestry for development and carbon mitigation","year":2012,"lang":"en","type":"article","venue":"Climatic Change","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Climate change mitigation; Climate change; Carbon sequestration; Business; Land use, land-use change and forestry; Land use; Environmental planning; Greenhouse gas; Environmental resource management; Agroforestry; Environmental science; Natural resource economics; Economics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001230031,0.0002727495,0.0002601507,0.0004453241,0.001974762,0.00457943,0.0003428363,0.002455017,0.009155991],"category_scores_gemma":[0.002040006,0.000116792,0.0001219886,0.0009043054,0.004028525,0.005491552,0.001429638,0.002553078,0.0005516761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001554013,"about_ca_system_score_gemma":0.002237895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009114305,"about_ca_topic_score_gemma":0.04892878,"domain_scores_codex":[0.9995952,0.0001568176,0.000007154082,0.00004476144,0.0001298745,0.00006620682],"domain_scores_gemma":[0.9992508,0.0002997979,0.00006110364,0.00005577336,0.000113836,0.0002186333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001614913,0.0001377784,0.01352564,0.0004896352,0.00004396982,0.000474449,0.004663485,0.001739859,0.004188734,0.4412711,0.1564433,0.3768606],"study_design_scores_gemma":[0.00003226511,0.00006545724,0.01858637,0.0004103093,0.00003902492,0.0006633696,0.01048828,0.001981661,0.00113906,0.6109089,0.3556306,0.00005467593],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0603861,0.1357808,0.0115432,0.6241615,0.003836771,0.00001654346,0.0002365477,0.0001434098,0.1638951],"genre_scores_gemma":[0.8779206,0.06767327,0.007070228,0.01990525,0.002658806,0.0000153177,0.00006560925,0.00005037512,0.02464062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009155991,"threshold_uncertainty_score":0.03062981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06621020938745137,"score_gpt":0.2405697895334607,"score_spread":0.1743595801460094,"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."}}