{"id":"W4295337148","doi":"10.1080/09614524.2022.2110571","title":"Retooling incentive mechanisms for effective smallholder tree growers’ contributions to global landscape restoration","year":2022,"lang":"en","type":"article","venue":"Development in Practice","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Cooperation and Exchange Programme; Department of Sport and Recreation, Northern Territory Government; Netherlands Institute of Government; Ministerium für Klimaschutz, Umwelt, Landwirtschaft, Natur- und Verbraucherschutz des Landes Nordrhein-Westfalen; International Interdisciplinary Laboratory for Advanced Functional Materials, Linköpings Universitet; Southern African Science Service Centre for Climate Change and Adaptive Land Management; Cultural Affairs and Missions Sector, Ministry of Higher Education; Foreign Affairs and International Trade Canada; Directorate-General for Migration and Home Affairs","keywords":"Incentive; Agroforestry; Tree (set theory); Natural resource economics; Business; Geography; Environmental planning; Economics; Environmental science; Market economy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02159609,0.0003109649,0.0002813927,0.00100979,0.003046327,0.004434777,0.001242836,0.002254889,0.01624255],"category_scores_gemma":[0.0374222,0.0003727832,0.0002562565,0.0005899739,0.004220553,0.004941011,0.007293412,0.002324474,0.0009310427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005947067,"about_ca_system_score_gemma":0.01564345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005343129,"about_ca_topic_score_gemma":0.01366977,"domain_scores_codex":[0.9909657,0.005931213,0.0002379121,0.0004724691,0.0007060434,0.001686634],"domain_scores_gemma":[0.9720222,0.01493069,0.003985056,0.002255546,0.001932578,0.00487396],"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":[0.0006268111,0.002116601,0.07799943,0.0007323169,0.00008915389,0.001290324,0.03382259,0.01345051,0.009402269,0.4737792,0.02453783,0.3621529],"study_design_scores_gemma":[0.00107765,0.001759513,0.09327457,0.002478665,0.0001372462,0.0005788518,0.04275119,0.03204873,0.004534686,0.3271181,0.4940041,0.0002367227],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6666141,0.001596675,0.07398949,0.06065136,0.0005146674,0.001927982,0.0001553996,0.0004511958,0.1940991],"genre_scores_gemma":[0.9850406,0.0001628925,0.009662792,0.001016968,0.00005021248,0.0002273657,0.00001235836,0.0000219539,0.003804767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02159609,"threshold_uncertainty_score":0.1142124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009988304451368338,"score_gpt":0.28268377588578,"score_spread":0.2726954714344117,"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."}}