{"id":"W2735983895","doi":"10.5558/tfc2017-024","title":"The potential of agroforestry to reduce atmospheric greenhouse gases in Canada: Insight from pairwise comparisons with traditional agriculture, data gaps and future research","year":2017,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Agroforestry and silvopastoral systems","field":"Agricultural and Biological Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Ministry of Agriculture and Forestry; Agriculture Food and Rural Development; University of Alberta","funders":"","keywords":"Greenhouse gas; Agriculture; Environmental science; Agroforestry; Vegetation (pathology); Carbon sequestration; Climate change mitigation; Soil carbon; Environmental protection; Soil water; Geography; Ecology; Carbon dioxide","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004137622,0.0001658117,0.0002184529,0.000002632022,0.001603212,0.0001658276,0.001686603,0.00007937889,0.00004426483],"category_scores_gemma":[0.0000475616,0.00004798195,0.00002892746,0.0002074908,0.0004208533,0.0002455802,0.0004543663,0.0003702714,0.000003623552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000130514,"about_ca_system_score_gemma":0.0002772435,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7781288,"about_ca_topic_score_gemma":0.953922,"domain_scores_codex":[0.9981657,0.0001969836,0.0002642632,0.0003726612,0.0005507939,0.0004496396],"domain_scores_gemma":[0.9987534,0.0003283989,0.000169789,0.0005131038,0.0000736361,0.0001616601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002071605,0.0005157324,0.7148896,0.00006678134,0.0002305944,0.0002645426,0.0005793338,0.001122101,0.02558596,0.003135128,0.1641347,0.08740388],"study_design_scores_gemma":[0.0002602841,0.0001807829,0.9721371,0.00006513111,0.00001114821,0.00002121958,0.001792587,0.0005448738,0.0003737191,0.0002208048,0.02426052,0.0001318131],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900062,0.002073797,9.90776e-7,0.006811408,0.0001507301,0.0003682026,0.0004346743,0.00001123582,0.0001427789],"genre_scores_gemma":[0.9984121,0.0002174543,0.00003791906,0.0000365444,0.001043703,0.00002671255,0.0001374636,0.000002333932,0.0000857261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2572475,"threshold_uncertainty_score":0.9996966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06718917566513903,"score_gpt":0.2604136829669892,"score_spread":0.1932245073018502,"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."}}