{"id":"W1998506661","doi":"10.1016/j.jenvman.2007.03.015","title":"Baseline assessment for environmental services payments from satellite imagery: A case study from Costa Rica and Mexico","year":2007,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Deforestation (computer science); Evergreen; Tropical and subtropical dry broadleaf forests; Baseline (sea); Forest cover; Deciduous; Geography; Satellite imagery; Evergreen forest; Ecosystem services; Forestry; Land cover; Forest ecology; Scale (ratio); Environmental science; Ecosystem; Environmental resource management; Remote sensing; Cartography; Land use; Ecology","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":[0.001607994,0.0002620639,0.0002952553,0.001067291,0.0005111276,0.001075288,0.0005819219,0.000612841,0.00123639],"category_scores_gemma":[0.004535524,0.0001704625,0.0003237709,0.002266388,0.0002264449,0.0006114901,0.0004602423,0.000356875,0.0001547175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002629898,"about_ca_system_score_gemma":0.001257469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2490384,"about_ca_topic_score_gemma":0.2552671,"domain_scores_codex":[0.9993426,0.0002943602,0.00004069889,0.0001019881,0.0001289498,0.00009140463],"domain_scores_gemma":[0.9977902,0.0007521396,0.0004542053,0.0002668376,0.0006548502,0.00008176159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008586436,0.001065054,0.9226862,0.0001233827,0.0002193438,0.00118257,0.001317358,0.01972333,0.002786889,0.00149274,0.00383902,0.04470544],"study_design_scores_gemma":[0.00005230651,0.0001251934,0.9717406,0.00002858994,0.00009376873,0.0001332975,0.002103379,0.0221993,0.0008220227,0.0002038679,0.002483215,0.00001446936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960904,0.00006841811,0.000468618,0.0001681126,0.000002510361,0.00004679296,0.001510851,0.00002210027,0.001622242],"genre_scores_gemma":[0.9955699,0.00006707654,0.002079026,0.00001995163,0.000003848946,0.00004533522,0.001749463,0.000005458995,0.0004600448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2490384,"threshold_uncertainty_score":0.4951776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008075235244451685,"score_gpt":0.2282482876179436,"score_spread":0.2201730523734919,"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."}}