{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004722659,0.00007912651,0.00008208227,0.00004101628,0.00009325732,0.00001077799,0.000124225,0.00002946671,0.0006181648],"category_scores_gemma":[0.00002506096,0.00005379314,0.00003824009,0.00008271397,0.000211562,0.0001512393,0.0002774047,0.00003026961,0.0001448526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005237323,"about_ca_system_score_gemma":0.000001804268,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006333828,"about_ca_topic_score_gemma":0.02353727,"domain_scores_codex":[0.9993881,0.00001970514,0.0001144917,0.0001706542,0.0001559202,0.0001511147],"domain_scores_gemma":[0.9996706,0.00003666,0.00005977586,0.000151844,0.000006501185,0.00007462614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001412733,0.00004360118,0.9916039,0.000007294899,0.000008331265,0.000003401115,0.0002396594,0.000008515985,0.00005736646,0.0000750269,0.001903389,0.006035407],"study_design_scores_gemma":[0.0003224781,0.0000532408,0.9692594,0.00002351666,0.00001310583,0.000001621186,0.00005421897,0.0002535419,0.00003300628,0.0003387166,0.02957439,0.00007272181],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963053,0.000009929691,0.0004450563,0.001122459,0.00009220336,0.0001971579,0.00001228926,0.00001781766,0.001797804],"genre_scores_gemma":[0.997822,0.00002519672,0.00008870653,0.0001197401,0.00004087314,0.00001361476,0.000004000439,0.000004870496,0.001881001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.027671,"threshold_uncertainty_score":0.9942806,"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."}}