{"id":"W4294365719","doi":"10.1088/1748-9326/ac8b9a","title":"Frontier metrics for a process-based understanding of deforestation dynamics","year":2022,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"H2020 European Research Council; Bundesministerium für Bildung und Forschung; Federalno Ministarstvo Obrazovanja i Nauke; Belgian Federal Science Policy Office; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Deforestation (computer science); Frontier; Woodland; Geography; Agriculture; Land use; Agricultural land; Land use, land-use change and forestry; Economic geography; Natural resource economics; Ecology; Economics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009510274,0.00009090052,0.00009335845,0.0002252873,0.0007630737,0.00002070995,0.000320677,0.00002101169,0.001412665],"category_scores_gemma":[0.00004085871,0.0000967943,0.00006919567,0.000444346,0.0003981148,0.00009400169,0.0003795249,0.0001654365,0.00001805194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002720638,"about_ca_system_score_gemma":0.00001122931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000109576,"about_ca_topic_score_gemma":0.00003779639,"domain_scores_codex":[0.9978982,0.0001367979,0.0001610648,0.0002899068,0.001186318,0.0003276679],"domain_scores_gemma":[0.9995134,0.0001591276,0.00007906085,0.0001791978,0.000002620371,0.00006657183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001673073,0.0002974443,0.952983,0.00004441639,0.00002759346,0.00000655144,0.0008634031,0.03635427,0.002767657,0.00005521829,0.003602772,0.002830384],"study_design_scores_gemma":[0.00338692,0.001002788,0.6308957,0.00001564615,0.00007524269,0.000002463313,0.03752923,0.2944967,0.001860649,0.0030502,0.02703706,0.0006474731],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700081,0.00002073547,0.02618505,0.002404788,0.00006059488,0.0007316907,0.0001261135,0.00001274485,0.000450142],"genre_scores_gemma":[0.9981223,0.000005313367,0.0008781458,0.0004156227,0.000009274291,0.0001006819,0.0001539474,0.00001310096,0.0003015881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3220873,"threshold_uncertainty_score":0.9995002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0590259483182404,"score_gpt":0.2850153673236421,"score_spread":0.2259894190054017,"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."}}