{"id":"W3202704478","doi":"10.1016/j.jenvman.2021.113817","title":"Towards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services","year":2021,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; Université de Sherbrooke","funders":"","keywords":"Provisioning; Ecosystem services; Sustainable forest management; Bayesian network; Logging; Forest management; Environmental resource management; Forest road; Sustainable management; Environmental science; Forestry; Computer science; Ecosystem; Agroforestry; Geography; Sustainability; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004501793,0.000252464,0.0003236814,0.00008151598,0.0003136497,0.0001208026,0.0003575366,0.00009637188,0.0005232144],"category_scores_gemma":[0.000004445705,0.000242516,0.000195567,0.0003684085,0.00006940922,0.0002902462,0.0004910238,0.0002253667,0.00007392644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007152349,"about_ca_system_score_gemma":0.00001450909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003029512,"about_ca_topic_score_gemma":0.0003259433,"domain_scores_codex":[0.9975926,0.0001100416,0.0006962023,0.0003776636,0.0006726542,0.0005508295],"domain_scores_gemma":[0.9988734,0.00001961662,0.0004024182,0.0003988167,0.000005649641,0.0003001202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001387053,0.001017473,0.1759615,0.0002634409,0.0006424334,0.002843526,0.001580037,0.7642276,0.00505477,0.0007990806,0.002554217,0.04491716],"study_design_scores_gemma":[0.00114379,0.0002750289,0.9102849,0.0002751959,0.0003571304,0.0003809512,0.00530137,0.03300561,0.0007624806,0.00193657,0.04571276,0.0005642101],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788646,0.0001157825,0.01325002,0.000535312,0.0002511445,0.0004863765,0.000007056777,0.00002017582,0.006469525],"genre_scores_gemma":[0.9877903,0.00005971255,0.01069588,0.000258901,0.0001470408,0.000003279895,0.00001097524,0.00003937156,0.000994556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7343234,"threshold_uncertainty_score":0.9889517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005213225137617926,"score_gpt":0.20941167458939,"score_spread":0.2041984494517721,"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."}}