{"id":"W4415882301","doi":"10.1007/978-3-031-34967-6_95","title":"Harnessing Nature to Address Climate Change: Agri-Environmental Approaches for Adaptation and Mitigation","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Agroforestry and silvopastoral systems","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Climate change; Corporate governance; Adaptation (eye); Greenhouse gas; Investment (military); Sustainable development; Order (exchange); Scale (ratio)","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":[0.0003219699,0.0004082322,0.0002002438,0.0003673449,0.0005212029,0.002757939,0.00055788,0.0009492066,0.01393667],"category_scores_gemma":[0.0002647803,0.0001073443,0.0001455497,0.0008500654,0.001912888,0.002519483,0.001092896,0.00113669,0.002204285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114187,"about_ca_system_score_gemma":0.001499696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002043607,"about_ca_topic_score_gemma":0.01276628,"domain_scores_codex":[0.9998908,0.00002999366,0.000002832433,0.00001362219,0.00004918361,0.00001347626],"domain_scores_gemma":[0.9999352,0.00003469519,0.00000485909,0.000006565903,0.00001051508,0.000008161305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001239122,0.00006088012,0.0001612656,0.0003347462,0.000008392108,0.00008251539,0.000781777,0.0009168337,0.001079042,0.7477038,0.08823632,0.1606221],"study_design_scores_gemma":[0.000002741608,0.00001099553,0.0003238141,0.0002279791,0.000003400234,0.00007522845,0.0005128117,0.0002769587,0.00017917,0.1522802,0.846102,0.000004799316],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002067727,0.08324663,0.007425736,0.01163391,0.001754201,0.00002773738,0.00006014586,0.00006011952,0.8937238],"genre_scores_gemma":[0.09841023,0.1790613,0.01630401,0.008260122,0.001773237,0.0001185009,0.0001587522,0.0001390995,0.6957748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01393667,"threshold_uncertainty_score":0.04662281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06340087559827665,"score_gpt":0.2268545462049194,"score_spread":0.1634536706066428,"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."}}