{"id":"W2288002285","doi":"10.3390/f7030055","title":"Tropical Forest Gain and Interactions amongst Agents of Forest Change","year":2016,"lang":"en","type":"article","venue":"Forests","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Smithsonian Tropical Research Institute; McGill University; University of Melbourne; Smithsonian Institution","keywords":"Reforestation; Deforestation (computer science); Forest restoration; Agroforestry; State forest; Geography; Forest management; Subsidy; Forestry; Business; Natural resource economics; Forest ecology; Ecology; Economics; Environmental science","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":[0.00257444,0.0003087308,0.0003230485,0.0009419978,0.002274739,0.00354913,0.000391676,0.0008990557,0.007831931],"category_scores_gemma":[0.00658911,0.0002190858,0.0002348208,0.0005729072,0.002773656,0.003332329,0.002588667,0.001080977,0.000278909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746884,"about_ca_system_score_gemma":0.0006724057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005234006,"about_ca_topic_score_gemma":0.00635287,"domain_scores_codex":[0.9980778,0.001204106,0.00005357018,0.0001917822,0.000176359,0.0002965144],"domain_scores_gemma":[0.994079,0.003658393,0.001288689,0.0002059692,0.0002331439,0.0005347952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006928011,0.0006978514,0.4418455,0.0003749634,0.000412043,0.003543082,0.1078359,0.01582517,0.003718711,0.3245918,0.002365512,0.09809671],"study_design_scores_gemma":[0.0001796047,0.0007701162,0.4103053,0.0002050388,0.0002605218,0.001764895,0.1698114,0.05946653,0.001050693,0.2846406,0.07139563,0.0001497337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9255356,0.0006628898,0.005989656,0.002461917,0.00002136499,0.00005625876,0.00005727062,0.00001265649,0.06520242],"genre_scores_gemma":[0.9981478,0.0001161929,0.0003878593,0.00002946871,0.000009325749,0.0000144649,0.00000950611,0.000001748644,0.001283693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007831931,"threshold_uncertainty_score":0.02620041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03028476789928684,"score_gpt":0.2320176279224491,"score_spread":0.2017328600231622,"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."}}