{"id":"W4402468794","doi":"10.1016/j.csag.2024.100019","title":"Site-based climate-smart tree species selection for forestation under climate change","year":2024,"lang":"en","type":"article","venue":"Climate smart agriculture.","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Asia-Pacific Network for Sustainable Forest Management and Rehabilitation","keywords":"Afforestation; Climate change; Selection (genetic algorithm); Tree (set theory); Environmental science; Ecology; Geography; Agroforestry; Environmental resource management; Computer science; Biology; Machine learning; Mathematics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007470187,0.000472089,0.0003777284,0.0001183519,0.0005250677,0.0003615862,0.0002278676,0.0002576448,0.0005297157],"category_scores_gemma":[0.00004100948,0.0003471251,0.0002889991,0.0007819192,0.00009758003,0.0006238759,0.0001386473,0.0002309555,0.002600688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006155595,"about_ca_system_score_gemma":0.00001269857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002306704,"about_ca_topic_score_gemma":0.007070819,"domain_scores_codex":[0.9969213,0.0001208643,0.0005007962,0.0008586356,0.0004879672,0.001110458],"domain_scores_gemma":[0.9990373,0.0002938198,0.0001672844,0.0002748002,0.00004270456,0.0001841393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007030557,0.0008361122,0.5518275,0.005634059,0.0002146288,0.00007125993,0.002432347,0.01482271,0.2856999,0.009110434,0.1096768,0.01897125],"study_design_scores_gemma":[0.001299807,0.0006883372,0.611779,0.000580563,0.0002741462,0.00005500905,0.0003196719,0.2670854,0.009796329,0.0001948201,0.1066022,0.001324728],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783615,0.0003847819,0.001283833,0.001454062,0.002913334,0.003448906,0.0007389532,0.00128615,0.01012847],"genre_scores_gemma":[0.9954631,0.0002373603,0.0008460786,0.0003708137,0.0007604467,0.0008503536,0.0009651781,0.00009085638,0.0004158049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2759036,"threshold_uncertainty_score":0.9998981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164310568906543,"score_gpt":0.2292835934596331,"score_spread":0.2128525365689788,"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."}}